Intelligent voice recognizing method, voice recognizing apparatus, intelligent computing device and server

ABSTRACT

Provided are an intelligent voice recognition method, a voice recognition device, and an intelligent computing device. A method of intelligently recognizing a voice in a voice recognition device includes obtaining an ambient noise signal from the microphone detection signal when a voice is detected from the microphone detection signal; updating a previously learned noise cancellation model based on the ambient noise; and removing the ambient noise signal from the microphone detection signal. Therefore, a voice recognition performance of the voice recognition device can be maximized. At least one of the voice recognition device, the intelligent computing device and the server of the present disclosure may be associated with an Artificial Intelligence module, a drone (Unmanned Aerial Vehicle, UAV), robot, Augmented Reality (AR) device, virtual reality (VR) device and a device related to the 5G service.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is based on and claims priority under 35 U.S.C. 119 to Korean Patent Application No. 10-2019-0091942, filed on Jul. 29, 2019, in the Korean Intellectual Property Office, the disclosure of which is herein incorporated by reference in its entirety.

BACKGROUND OF THE INVENTION Field of the Invention

The present disclosure relates to an intelligent speech recognition method, a speech recognition device, an intelligent computing device and a server, and more particularly, to an intelligent speech recognition method, a speech recognition device, an intelligent computing device and a server for removing ambient noise signals.

Related Art

A voice recognition device is a device that converts a user's voice into a text and that analyzes a meaning of a message included in the text and that outputs another form of sound based on the analysis result.

A voice recognition device includes, for example, an IoT device such as a home robot and an artificial intelligence (AI) speaker of a home IoT system.

A noise and reverberation cancellation model for improving an existing voice recognition performance is distributed in the form of mounting an already learned model in a voice recognition device or an external server when a product of the voice recognition device is launched.

Because the noise and reverberation cancellation model is not a model optimized for a noise (noise or reverberation in which noise is reflected) of an environment in which the voice recognition device is located, there is a problem that a best voice recognition performance may not be exhibited in a current environment of the voice recognition device.

SUMMARY OF THE INVENTION

An object of the present disclosure is to meet the needs and solve the problems.

Further, the present disclosure provides an optimal intelligent voice recognition method, a voice recognition device, an intelligent computing device, and a server for effectively removing an ambient noise signal from a user's voice based on an ambient noise at a location in which a voice recognition device is currently located.

In an aspect, a method of intelligently recognizing a voice of a voice recognition device includes obtaining a microphone detection signal detected using at least one microphone of the voice recognition device; obtaining an ambient noise signal from the microphone detection signal when a voice signal is detected from the microphone detection signal; updating a previously learned noise cancellation model based on the ambient noise; and removing the ambient noise signal from the microphone detection signal using the updated noise cancellation model.

The obtaining of an ambient noise signal may include obtaining a signal of a non-voice segment other than a voice segment in which the voice is detected among an entire segment of the microphone detection signal as the ambient noise signal.

The updating of the previously learned noise cancellation model may include updating the noise cancellation model in every predetermined period.

The period may be changed based on a capacity in which the ambient noise signal is stored in a memory of the voice recognition device.

The obtaining of an ambient noise signal may include inputting the microphone detection signal to the previously learned voice detection model; determining whether the voice is detected from the microphone detection signal based on an output value of the voice detection model; and obtaining the ambient noise signal from the microphone detection signal when the voice is detected from the microphone detection signal.

The noise cancellation model is previously learned based on another noise signal of a predetermined space and another voice signal of predetermined another user, wherein the updating of the previously learned noise cancellation model may include updating the previously learned noise cancellation model using the another voice signal and the ambient noise signal.

The method may further include receiving Downlink Control Information (DCI) used for scheduling transmission of the microphone detection signal from a network; and transmitting the microphone detection signal to the network based on the DCI.

The method may further include performing an initial access procedure to the network based on a synchronization signal block (SSB); and transmitting the microphone detection signal to the network through a Physical Uplink Shared Channel (PUSCH), wherein a DM-RS of the PUSCH and the SSB is quasi co-located (QCL) for QCL type D.

The method may further include controlling a communication unit to transmit the microphone detection signal to an AI processor included in the network; and controlling the communication unit to receive AI processed information from the AI processor, wherein the AI processed information may include data related to updating of the noise cancellation model performed based on the microphone detection signal.

The data related to updating of the noise cancellation model may include the updated noise cancellation model.

The data related to updating of the noise cancellation model may include the updated model parameter of the noise cancellation model.

The data related to updating of the noise cancellation model may include the ambient noise signal.

The data related to updating of the noise cancellation model may include a microphone detection signal in which the ambient noise signal is removed.

The method may further include controlling a communication unit to transmit the ambient noise signal to an AI processor included in the network; and controlling the communication unit to receive AI processed information from the AI processor, wherein the AI processed information may include data related to updating of the noise cancellation model performed based on the ambient noise signal.

In another aspect, a voice recognition device for intelligently recognizing a voice includes a communication unit for transmitting and receiving data to and from a network; at least one microphone; and a processor for obtaining a detection signal of the microphone, obtaining an ambient noise signal from the microphone detection signal when a voice signal is detected from the microphone detection signal, updating a previously learned noise cancellation model based on the ambient noise signal, and removing the ambient noise signal from the microphone detection signal using the updated noise cancellation model.

The processor may obtain a signal of a non-voice segment other than a voice segment in which the voice is detected among an entire segment of the microphone detection signal as the ambient noise signal.

The processor may update the noise cancellation model in every predetermined period.

The period may be changed based on a capacity in which the ambient noise signal is stored in a memory of the voice recognition device.

The processor may input the microphone detection signal to the previously learned voice detection model, determine whether the voice is detected from the microphone detection signal based on an output value of the voice detection model, and obtain the ambient noise signal from the microphone detection signal when the voice is detected from the microphone detection signal.

The noise cancellation model may be previously learned based on another noise signal of a predetermined space and another voice signal of a predetermined another user, wherein the processor may update the previously learned noise cancellation model using the another voice signal and the ambient noise signal.

The processor may control the communication unit to receive Downlink Control Information (DCI) used for scheduling transmission of the microphone detection signal from a network and to transmit the microphone detection signal to the network based on the DCI.

The processor may control the communication unit to perform an initial access procedure to the network based on a synchronization signal block (SSB) and to transmit the microphone detection signal to the network through a PUSCH, and a DM-RS of the PUSCH and the SSB is QCL for QCL type D.

The processor may control the communication unit to transmit the microphone detection signal to an AI processor included in the network, and to receive AI processed information from the AI processor, and the AI processed information may include data related to updating of the noise cancellation model performed based on the microphone detection signal.

Data related to updating of the noise cancellation model may include the updated noise cancellation model.

Data related to updating of the noise cancellation model may include updated model parameters of the noise cancellation model.

Data related to updating of the noise cancellation model may include the ambient noise signal.

Data related to updating of the noise cancellation model may include a microphone detection signal in which the ambient noise signal is removed.

The processor may control the communication unit to transmit the ambient noise signal to the AI processor included in the network and to receive the AI processed information from the AI processor, and the AI processed information may include data related to updating of the noise cancellation model performed based on the ambient noise signal.

In another aspect, a non-transitory computer readable recording medium is a non-transitory computer-executable component that stores a computer-executable component configured to execute in at least one processor of a computing device and obtains a microphone detection signal, obtains an ambient noise signal from the microphone detection signal when a user's voice is detected from the microphone detection signal, obtains an ambient noise signal from the microphone detection signal when the voice signal is detected from the microphone detection signal, updates a previously learned noise cancellation model based on the ambient noise, and removes the ambient noise signal from the microphone detection signal using the updated noise cancellation model.

In another aspect, a server for intelligently recognizing a voice includes a communication unit for transmitting and receiving data to and from a voice recognition device; and a processor for controlling the communication unit to obtain a microphone detection signal detected by at least one microphone of the voice recognition device and to obtain an ambient noise signal from the microphone detection signal when a voice signal is detected from the microphone detection signal, to update the previously learned noise cancellation model based on the ambient noise signal, to obtain the voice in which the ambient noise signal is removed using the updated noise cancellation model and for controlling the communication unit to transmit data related to the updated noise cancellation model to the voice recognition device.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, included as part of the detailed description in order to provide a thorough understanding of the present disclosure, provide embodiments of the present disclosure and together with the description, describe the technical features of the present disclosure.

FIG. 1 is a block diagram of a wireless communication system to which methods proposed in the disclosure are applicable.

FIG. 2 shows an example of a signal transmission/reception method in a wireless communication system.

FIG. 3 shows an example of basic operations of an user equipment and a 5G network in a 5G communication system.

FIG. 4 shows an example of a schematic block diagram in which a text-to-speech (TTS) method according to an embodiment of the present disclosure is implemented.

FIG. 5 shows a block diagram of an AI device that may be applied to one embodiment of the present disclosure.

FIG. 6 is an exemplary block diagram of a voice recognition apparatus according to an embodiment of the present disclosure.

FIG. 7 shows a schematic block diagram of a text-to-speech (TTS) device in a TTS system according to an embodiment of the present disclosure.

FIG. 8 is a schematic block diagram of a TTS device in a TTS system environment according to an embodiment of the present disclosure.

FIG. 9 is a schematic block diagram of an AI processor capable of performing emotion classification information-based TTS according to an embodiment of the present disclosure.

FIG. 10 is a flowchart illustrating a voice recognition method according to an embodiment of the present disclosure.

FIG. 11 is a diagram illustrating a process in which a voice recognition device obtains an ambient noise signal.

FIG. 12 is a diagram illustrating a process in which a voice recognition device performs transfer learning (updating).

FIG. 13 is a diagram illustrating a process in which a voice recognition device divides a microphone detection signal according to a time segment.

FIG. 14 is a diagram illustrating a process in which a voice recognition device updates a noise cancellation model.

FIG. 15 is a flowchart illustrating a detailed example of a process in which a voice recognition device obtains an ambient noise signal.

FIG. 16 is a flowchart illustrating a detailed example of a process in which a voice recognition device updates a noise cancellation model.

FIG. 17 illustrates one example of a process in which a voice recognition device updates a noise cancellation model in cooperation with a server (5G network).

FIG. 18 illustrates another example of a process in which a voice recognition device updates a noise cancellation model in cooperation with a server (5G network).

FIG. 19 illustrates another example of a process in which a voice recognition device updates a noise cancellation model in cooperation with a server (5G network).

FIG. 20 illustrates another example of a process in which a voice recognition device updates a noise cancellation model in cooperation with a server (5G network).

FIG. 21 illustrates another example of a process in which a voice recognition device updates a noise cancellation model in cooperation with a server (5G network).

FIG. 22 illustrates a server for recognizing a voice according to another embodiment of the present disclosure.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

Hereinafter, embodiments of the disclosure will be described in detail with reference to the attached drawings. The same or similar components are given the same reference numbers and redundant description thereof is omitted. The suffixes “module” and “unit” of elements herein are used for convenience of description and thus can be used interchangeably and do not have any distinguishable meanings or functions. Further, in the following description, if a detailed description of known techniques associated with the present disclosure would unnecessarily obscure the gist of the present disclosure, detailed description thereof will be omitted. In addition, the attached drawings are provided for easy understanding of embodiments of the disclosure and do not limit technical spirits of the disclosure, and the embodiments should be construed as including all modifications, equivalents, and alternatives falling within the spirit and scope of the embodiments.

While terms, such as “first”, “second”, etc., may be used to describe various components, such components must not be limited by the above terms. The above terms are used only to distinguish one component from another.

When an element is “coupled” or “connected” to another element, it should be understood that a third element may be present between the two elements although the element may be directly coupled or connected to the other element. When an element is “directly coupled” or “directly connected” to another element, it should be understood that no element is present between the two elements.

The singular forms are intended to include the plural forms as well, unless the context clearly indicates otherwise.

In addition, in the specification, it will be further understood that the terms “comprise” and “include” specify the presence of stated features, integers, steps, operations, elements, components, and/or combinations thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or combinations.

Hereinafter, 5G communication (5th generation mobile communication) required by an apparatus requiring AI processed information and/or an AI processor will be described through paragraphs A through G.

A. Example of Block Diagram of UE and 5G Network

FIG. 1 is a block diagram of a wireless communication system to which methods proposed in the disclosure are applicable.

Referring to FIG. 1, a device (AI device) including an AI module is defined as a first communication device (910 of FIG. 1), and a processor 911 can perform detailed AI operation.

A 5G network including another device (AI server) communicating with the AI device is defined as a second communication device (920 of FIG. 1), and a processor 921 can perform detailed AI operations.

The 5G network may be represented as the first communication device and the AI device may be represented as the second communication device.

For example, the first communication device or the second communication device may be a base station, a network node, a transmission terminal, a reception terminal, a wireless device, a wireless communication device, an autonomous device, or the like.

For example, the first communication device or the second communication device may be a base station, a network node, a transmission terminal, a reception terminal, a wireless device, a wireless communication device, a vehicle, a vehicle having an autonomous function, a connected car, a drone (Unmanned Aerial Vehicle, UAV), and AI (Artificial Intelligence) module, a robot, an AR (Augmented Reality) device, a VR (Virtual Reality) device, an MR (Mixed Reality) device, a hologram device, a public safety device, an MTC device, an IoT device, a medical device, a Fin Tech device (or financial device), a security device, a climate/environment device, a device associated with 5G services, or other devices associated with the fourth industrial revolution field.

For example, a terminal or user equipment (UE) may include a cellular phone, a smart phone, a laptop computer, a digital broadcast terminal, personal digital assistants (PDAs), a portable multimedia player (PMP), a navigation device, a slate PC, a tablet PC, an ultrabook, a wearable device (e.g., a smartwatch, a smart glass and a head mounted display (HMD)), etc. For example, the HMD may be a display device worn on the head of a user. For example, the HMD may be used to realize VR, AR or MR. For example, the drone may be a flying object that flies by wireless control signals without a person therein. For example, the VR device may include a device that implements objects or backgrounds of a virtual world. For example, the AR device may include a device that connects and implements objects or background of a virtual world to objects, backgrounds, or the like of a real world. For example, the MR device may include a device that unites and implements objects or background of a virtual world to objects, backgrounds, or the like of a real world. For example, the hologram device may include a device that implements 360-degree 3D images by recording and playing 3D information using the interference phenomenon of light that is generated by two lasers meeting each other which is called holography. For example, the public safety device may include an image repeater or an imaging device that can be worn on the body of a user. For example, the MTC device and the IoT device may be devices that do not require direct interference or operation by a person. For example, the MTC device and the IoT device may include a smart meter, a bending machine, a thermometer, a smart bulb, a door lock, various sensors, or the like. For example, the medical device may be a device that is used to diagnose, treat, attenuate, remove, or prevent diseases. For example, the medical device may be a device that is used to diagnose, treat, attenuate, or correct injuries or disorders. For example, the medial device may be a device that is used to examine, replace, or change structures or functions. For example, the medical device may be a device that is used to control pregnancy. For example, the medical device may include a device for medical treatment, a device for operations, a device for (external) diagnose, a hearing aid, an operation device, or the like. For example, the security device may be a device that is installed to prevent a danger that is likely to occur and to keep safety. For example, the security device may be a camera, a CCTV, a recorder, a black box, or the like. For example, the Fin Tech device may be a device that can provide financial services such as mobile payment.

Referring to FIG. 1, the first communication device 910 and the second communication device 920 include processors 911 and 921, memories 914 and 924, one or more Tx/Rx radio frequency (RF) modules 915 and 925, Tx processors 912 and 922, Rx processors 913 and 923, and antennas 916 and 926. The Tx/Rx module is also referred to as a transceiver. Each Tx/Rx module 915 transmits a signal through each antenna 926. The processor implements the aforementioned functions, processes and/or methods. The processor 921 may be related to the memory 924 that stores program code and data. The memory may be referred to as a computer-readable medium. More specifically, the Tx processor 912 implements various signal processing functions with respect to L1 (i.e., physical layer) in DL (communication from the first communication device to the second communication device). The Rx processor implements various signal processing functions of L1 (i.e., physical layer).

UL (communication from the second communication device to the first communication device) is processed in the first communication device 910 in a way similar to that described in association with a receiver function in the second communication device 920. Each Tx/Rx module 925 receives a signal through each antenna 926. Each Tx/Rx module provides RF carriers and information to the Rx processor 923. The processor 921 may be related to the memory 924 that stores program code and data. The memory may be referred to as a computer-readable medium.

B. Signal Transmission/Reception Method in Wireless Communication System

FIG. 2 is a diagram showing an example of a signal transmission/reception method in a wireless communication system.

Referring to FIG. 2, when a UE is powered on or enters a new cell, the UE performs an initial cell search operation such as synchronization with a BS (S201). For this operation, the UE can receive a primary synchronization channel (P-SCH) and a secondary synchronization channel (S-SCH) from the BS to synchronize with the BS and obtain information such as a cell ID. In LTE and NR systems, the P-SCH and S-SCH are respectively called a primary synchronization signal (PSS) and a secondary synchronization signal (SSS). After initial cell search, the UE can obtain broadcast information in the cell by receiving a physical broadcast channel (PBCH) from the BS. Further, the UE can receive a downlink reference signal (DL RS) in the initial cell search step to check a downlink channel state. After initial cell search, the UE can obtain more detailed system information by receiving a physical downlink shared channel (PDSCH) according to a physical downlink control channel (PDCCH) and information included in the PDCCH (S202).

Meanwhile, when the UE initially accesses the BS or has no radio resource for signal transmission, the UE can perform a random access procedure (RACH) for the BS (steps S203 to S206). To this end, the UE can transmit a specific sequence as a preamble through a physical random access channel (PRACH) (S203 and S205) and receive a random access response (RAR) message for the preamble through a PDCCH and a corresponding PDSCH (S204 and S206). In the case of a contention-based RACH, a contention resolution procedure may be additionally performed.

After the UE performs the above-described process, the UE can perform PDCCH/PDSCH reception (S207) and physical uplink shared channel (PUSCH)/physical uplink control channel (PUCCH) transmission (S208) as normal uplink/downlink signal transmission processes. Particularly, the UE receives downlink control information (DCI) through the PDCCH. The UE monitors a set of PDCCH candidates in monitoring occasions set for one or more control element sets (CORESET) on a serving cell according to corresponding search space configurations. A set of PDCCH candidates to be monitored by the UE is defined in terms of search space sets, and a search space set may be a common search space set or a UE-specific search space set. CORESET includes a set of (physical) resource blocks having a duration of one to three OFDM symbols. A network can configure the UE such that the UE has a plurality of CORESETs. The UE monitors PDCCH candidates in one or more search space sets. Here, monitoring means attempting decoding of PDCCH candidate(s) in a search space. When the UE has successfully decoded one of PDCCH candidates in a search space, the UE determines that a PDCCH has been detected from the PDCCH candidate and performs PDSCH reception or PUSCH transmission on the basis of DCI in the detected PDCCH. The PDCCH can be used to schedule DL transmissions over a PDSCH and UL transmissions over a PUSCH. Here, the DCI in the PDCCH includes downlink assignment (i.e., downlink grant (DL grant)) related to a physical downlink shared channel and including at least a modulation and coding format and resource allocation information, or an uplink grant (UL grant) related to a physical uplink shared channel and including a modulation and coding format and resource allocation information.

An initial access (IA) procedure in a 5G communication system will be additionally described with reference to FIG. 2.

The UE can perform cell search, system information acquisition, beam alignment for initial access, and DL measurement on the basis of an SSB. The SSB is interchangeably used with a synchronization signal/physical broadcast channel (SS/PBCH) block.

The SSB includes a PSS, an SSS and a PBCH. The SSB is configured in four consecutive OFDM symbols, and a PSS, a PBCH, an SSS/PBCH or a PBCH is transmitted for each OFDM symbol. Each of the PSS and the SSS includes one OFDM symbol and 127 subcarriers, and the PBCH includes 3 OFDM symbols and 576 subcarriers.

Cell search refers to a process in which a UE obtains time/frequency synchronization of a cell and detects a cell identifier (ID) (e.g., physical layer cell ID (PCI)) of the cell. The PSS is used to detect a cell ID in a cell ID group and the SSS is used to detect a cell ID group. The PBCH is used to detect an SSB (time) index and a half-frame.

There are 336 cell ID groups and there are 3 cell IDs per cell ID group. A total of 1008 cell IDs are present. Information on a cell ID group to which a cell ID of a cell belongs is provided/obtained through an SSS of the cell, and information on the cell ID among 336 cell ID groups is provided/obtained through a PSS.

The SSB is periodically transmitted in accordance with SSB periodicity. A default SSB periodicity assumed by a UE during initial cell search is defined as 20 ms. After cell access, the SSB periodicity can be set to one of {5 ms, 10 ms, 20 ms, 40 ms, 80 ms, 160 ms} by a network (e.g., a BS).

Next, acquisition of system information (SI) will be described.

SI is divided into a master information block (MIB) and a plurality of system information blocks (SIBs). SI other than the MIB may be referred to as remaining minimum system information. The MIB includes information/parameter for monitoring a PDCCH that schedules a PDSCH carrying SIB1 (SystemInformationBlock1) and is transmitted by a BS through a PBCH of an SSB. SIB1 includes information related to availability and scheduling (e.g., transmission periodicity and SI-window size) of the remaining SIBs (hereinafter, SIBx, xis an integer equal to or greater than 2). SiBx is included in an SI message and transmitted over a PDSCH. Each SI message is transmitted within a periodically generated time window (i.e., SI-window).

A random access (RA) procedure in a 5G communication system will be additionally described with reference to FIG. 2.

A random access procedure is used for various purposes. For example, the random access procedure can be used for network initial access, handover, and UE-triggered UL data transmission. A UE can obtain UL synchronization and UL transmission resources through the random access procedure. The random access procedure is classified into a contention-based random access procedure and a contention-free random access procedure. A detailed procedure for the contention-based random access procedure is as follows.

A UE can transmit a random access preamble through a PRACH as Msg1 of a random access procedure in UL. Random access preamble sequences having different two lengths are supported. A long sequence length 839 is applied to subcarrier spacings of 1.25 kHz and 5 kHz and a short sequence length 139 is applied to subcarrier spacings of 15 kHz, 30 kHz, 60 kHz and 120 kHz.

When a BS receives the random access preamble from the UE, the BS transmits a random access response (RAR) message (Msg2) to the UE. A PDCCH that schedules a PDSCH carrying a RAR is CRC masked by a random access (RA) radio network temporary identifier (RNTI) (RA-RNTI) and transmitted. Upon detection of the PDCCH masked by the RA-RNTI, the UE can receive a RAR from the PDSCH scheduled by DCI carried by the PDCCH. The UE checks whether the RAR includes random access response information with respect to the preamble transmitted by the UE, that is, Msg1. Presence or absence of random access information with respect to Msg1 transmitted by the UE can be determined according to presence or absence of a random access preamble ID with respect to the preamble transmitted by the UE. If there is no response to Msg1, the UE can retransmit the RACH preamble less than a predetermined number of times while performing power ramping. The UE calculates PRACH transmission power for preamble retransmission on the basis of most recent pathloss and a power ramping counter.

The UE can perform UL transmission through Msg3 of the random access procedure over a physical uplink shared channel on the basis of the random access response information. Msg3 can include an RRC connection request and a UE ID. The network can transmit Msg4 as a response to Msg3, and Msg4 can be handled as a contention resolution message on DL. The UE can enter an RRC connected state by receiving Msg4.

C. Beam Management (BM) Procedure of 5G Communication System

A BM procedure can be divided into (1) a DL MB procedure using an SSB or a CSI-RS and (2) a UL BM procedure using a sounding reference signal (SRS). In addition, each BM procedure can include Tx beam swiping for determining a Tx beam and Rx beam swiping for determining an Rx beam.

The DL BM procedure using an SSB will be described.

Configuration of a beam report using an SSB is performed when channel state information (CSI)/beam is configured in RRC_CONNECTED.

-   -   A UE receives a CSI-ResourceConfig IE including         CSI-SSB-ResourceSetList for SSB resources used for BM from a BS.         The RRC parameter “csi-SSB-ResourceSetList” represents a list of         SSB resources used for beam management and report in one         resource set. Here, an SSB resource set can be set as {SSBx1,         SSBx2, SSBx3, SSBx4, . . . }. An SSB index can be defined in the         range of 0 to 63.     -   The UE receives the signals on SSB resources from the BS on the         basis of the CSI-SSB-ResourceSetList.     -   When CSI-RS reportConfig with respect to a report on SSBRI and         reference signal received power (RSRP) is set, the UE reports         the best SSBRI and RSRP corresponding thereto to the BS. For         example, when reportQuantity of the CSI-RS reportConfig IE is         set to ‘ssb-Index-RSRP’, the UE reports the best SSBRI and RSRP         corresponding thereto to the BS.

When a CSI-RS resource is configured in the same OFDM symbols as an SSB and ‘QCL-TypeD’ is applicable, the UE can assume that the CSI-RS and the SSB are quasi co-located (QCL) from the viewpoint of ‘QCL-TypeD’. Here, QCL-TypeD may mean that antenna ports are quasi co-located from the viewpoint of a spatial Rx parameter. When the UE receives signals of a plurality of DL antenna ports in a QCL-TypeD relationship, the same Rx beam can be applied.

Next, a DL BM procedure using a CSI-RS will be described.

An Rx beam determination (or refinement) procedure of a UE and a Tx beam swiping procedure of a BS using a CSI-RS will be sequentially described. A repetition parameter is set to ‘ON’ in the Rx beam determination procedure of a UE and set to ‘OFF’ in the Tx beam swiping procedure of a BS.

First, the Rx beam determination procedure of a UE will be described.

-   -   The UE receives an NZP CSI-RS resource set IE including an RRC         parameter with respect to ‘repetition’ from a BS through RRC         signaling. Here, the RRC parameter ‘repetition’ is set to ‘ON’.     -   The UE repeatedly receives signals on resources in a CSI-RS         resource set in which the RRC parameter ‘repetition’ is set to         ‘ON’ in different OFDM symbols through the same Tx beam (or DL         spatial domain transmission filters) of the BS.     -   The UE determines an RX beam thereof.     -   The UE skips a CSI report. That is, the UE can skip a CSI report         when the RRC parameter ‘repetition’ is set to ‘ON’.

Next, the Tx beam determination procedure of a BS will be described.

-   -   A UE receives an NZP CSI-RS resource set IE including an RRC         parameter with respect to ‘repetition’ from the BS through RRC         signaling. Here, the RRC parameter ‘repetition’ is related to         the Tx beam swiping procedure of the BS when set to ‘OFF’.     -   The UE receives signals on resources in a CSI-RS resource set in         which the RRC parameter ‘repetition’ is set to ‘OFF’ in         different DL spatial domain transmission filters of the BS.     -   The UE selects (or determines) a best beam.     -   The UE reports an ID (e.g., CRI) of the selected beam and         related quality information (e.g., RSRP) to the BS. That is,         when a CSI-RS is transmitted for BM, the UE reports a CRI and         RSRP with respect thereto to the BS.

Next, the UL BM procedure using an SRS will be described.

-   -   A UE receives RRC signaling (e.g., SRS-Config IE) including a         (RRC parameter) purpose parameter set to ‘beam management” from         a BS. The SRS-Config IE is used to set SRS transmission. The         SRS-Config IE includes a list of SRS-Resources and a list of         SRS-ResourceSets. Each SRS resource set refers to a set of         SRS-resources.

The UE determines Tx beamforming for SRS resources to be transmitted on the basis of SRS-SpatialRelation Info included in the SRS-Config IE. Here, SRS-SpatialRelation Info is set for each SRS resource and indicates whether the same beamforming as that used for an SSB, a CSI-RS or an SRS will be applied for each SRS resource.

-   -   When SRS-SpatialRelationInfo is set for SRS resources, the same         beamforming as that used for the SSB, CSI-RS or SRS is applied.         However, when SRS-SpatialRelationInfo is not set for SRS         resources, the UE arbitrarily determines Tx beamforming and         transmits an SRS through the determined Tx beamforming.

Next, a beam failure recovery (BFR) procedure will be described.

In a beamformed system, radio link failure (RLF) may frequently occur due to rotation, movement or beamforming blockage of a UE. Accordingly, NR supports BFR in order to prevent frequent occurrence of RLF. BFR is similar to a radio link failure recovery procedure and can be supported when a UE knows new candidate beams. For beam failure detection, a BS configures beam failure detection reference signals for a UE, and the UE declares beam failure when the number of beam failure indications from the physical layer of the UE reaches a threshold set through RRC signaling within a period set through RRC signaling of the BS. After beam failure detection, the UE triggers beam failure recovery by initiating a random access procedure in a PCell and performs beam failure recovery by selecting a suitable beam. (When the BS provides dedicated random access resources for certain beams, these are prioritized by the UE). Completion of the aforementioned random access procedure is regarded as completion of beam failure recovery.

D. URLLC (Ultra-Reliable and Low Latency Communication)

URLLC transmission defined in NR can refer to (1) a relatively low traffic size, (2) a relatively low arrival rate, (3) extremely low latency requirements (e.g., 0.5 and 1 ms), (4) relatively short transmission duration (e.g., 2 OFDM symbols), (5) urgent services/messages, etc. In the case of UL, transmission of traffic of a specific type (e.g., URLLC) needs to be multiplexed with another transmission (e.g., eMBB) scheduled in advance in order to satisfy more stringent latency requirements. In this regard, a method of providing information indicating preemption of specific resources to a UE scheduled in advance and allowing a URLLC UE to use the resources for UL transmission is provided.

NR supports dynamic resource sharing between eMBB and URLLC. eMBB and URLLC services can be scheduled on non-overlapping time/frequency resources, and URLLC transmission can occur in resources scheduled for ongoing eMBB traffic. An eMBB UE may not ascertain whether PDSCH transmission of the corresponding UE has been partially punctured and the UE may not decode a PDSCH due to corrupted coded bits. In view of this, NR provides a preemption indication. The preemption indication may also be referred to as an interrupted transmission indication.

With regard to the preemption indication, a UE receives DownlinkPreemption IE through RRC signaling from a BS. When the UE is provided with DownlinkPreemption IE, the UE is configured with INT-RNTI provided by a parameter int-RNTI in DownlinkPreemption IE for monitoring of a PDCCH that conveys DCI format 2_1. The UE is additionally configured with a corresponding set of positions for fields in DCI format 2_1 according to a set of serving cells and positionlnDCI by INT-ConfigurationPerServing Cell including a set of serving cell indexes provided by servingCellID, configured having an information payload size for DCI format 2_1 according to dci-Payloadsize, and configured with indication granularity of time-frequency resources according to timeFrequencySect.

The UE receives DCI format 2_1 from the BS on the basis of the DownlinkPreemption IE.

When the UE detects DCI format 2_1 for a serving cell in a configured set of serving cells, the UE can assume that there is no transmission to the UE in PRBs and symbols indicated by the DCI format 2_1 in a set of PRBs and a set of symbols in a last monitoring period before a monitoring period to which the DCI format 2_1 belongs. For example, the UE assumes that a signal in a time-frequency resource indicated according to preemption is not DL transmission scheduled therefor and decodes data on the basis of signals received in the remaining resource region.

E. mMTC (Massive MTC)

mMTC (massive Machine Type Communication) is one of 5G scenarios for supporting a hyper-connection service providing simultaneous communication with a large number of UEs. In this environment, a UE intermittently performs communication with a very low speed and mobility. Accordingly, a main goal of mMTC is operating a UE for a long time at a low cost. With respect to mMTC, 3GPP deals with MTC and NB (NarrowBand)-IoT.

mMTC has features such as repetitive transmission of a PDCCH, a PUCCH, a PDSCH (physical downlink shared channel), a PUSCH, etc., frequency hopping, retuning, and a guard period.

That is, a PUSCH (or a PUCCH (particularly, a long PUCCH) or a PRACH) including specific information and a PDSCH (or a PDCCH) including a response to the specific information are repeatedly transmitted. Repetitive transmission is performed through frequency hopping, and for repetitive transmission, (RF) retuning from a first frequency resource to a second frequency resource is performed in a guard period and the specific information and the response to the specific information can be transmitted/received through a narrowband (e.g., 6 resource blocks (RBs) or 1 RB).

F. Basic Operation of AI Processing Using 5G Communication

FIG. 3 shows an example of basic operations of AI processing in a 5G communication system.

The UE transmits specific information to the 5G network (S1). The 5G network may perform 5G processing related to the specific information (S2). Here, the 5G processing may include AI processing. And the 5G network may transmit response including AI processing result to UE (S3).

G. Applied Operations Between UE and 5G Network in 5G Communication System

Hereinafter, the operation of an autonomous vehicle using 5G communication will be described in more detail with reference to wireless communication technology (BM procedure, URLLC, mMTC, etc.) described in FIGS. 1 and 2.

First, a basic procedure of an applied operation to which a method proposed by the present disclosure which will be described later and eMBB of 5G communication are applied will be described.

As in steps S1 and S3 of FIG. 3, the autonomous vehicle performs an initial access procedure and a random access procedure with the 5G network prior to step S1 of FIG. 3 in order to transmit/receive signals, information and the like to/from the 5G network.

More specifically, the autonomous vehicle performs an initial access procedure with the 5G network on the basis of an SSB in order to obtain DL synchronization and system information. A beam management (BM) procedure and a beam failure recovery procedure may be added in the initial access procedure, and quasi-co-location (QCL) relation may be added in a process in which the autonomous vehicle receives a signal from the 5G network.

In addition, the autonomous vehicle performs a random access procedure with the 5G network for UL synchronization acquisition and/or UL transmission. The 5G network can transmit, to the autonomous vehicle, a UL grant for scheduling transmission of specific information. Accordingly, the autonomous vehicle transmits the specific information to the 5G network on the basis of the UL grant. In addition, the 5G network transmits, to the autonomous vehicle, a DL grant for scheduling transmission of 5G processing results with respect to the specific information. Accordingly, the 5G network can transmit, to the autonomous vehicle, information (or a signal) related to remote control on the basis of the DL grant.

Next, a basic procedure of an applied operation to which a method proposed by the present disclosure which will be described later and URLLC of 5G communication are applied will be described.

As described above, an autonomous vehicle can receive DownlinkPreemption IE from the 5G network after the autonomous vehicle performs an initial access procedure and/or a random access procedure with the 5G network. Then, the autonomous vehicle receives DCI format 2_1 including a preemption indication from the 5G network on the basis of DownlinkPreemption IE. The autonomous vehicle does not perform (or expect or assume) reception of eMBB data in resources (PRBs and/or OFDM symbols) indicated by the preemption indication. Thereafter, when the autonomous vehicle needs to transmit specific information, the autonomous vehicle can receive a UL grant from the 5G network.

Next, a basic procedure of an applied operation to which a method proposed by the present disclosure which will be described later and mMTC of 5G communication are applied will be described.

Description will focus on parts in the steps of FIG. 3 which are changed according to application of mMTC.

In step S1 of FIG. 3, the autonomous vehicle receives a UL grant from the 5G network in order to transmit specific information to the 5G network. Here, the UL grant may include information on the number of repetitions of transmission of the specific information and the specific information may be repeatedly transmitted on the basis of the information on the number of repetitions. That is, the autonomous vehicle transmits the specific information to the 5G network on the basis of the UL grant. Repetitive transmission of the specific information may be performed through frequency hopping, the first transmission of the specific information may be performed in a first frequency resource, and the second transmission of the specific information may be performed in a second frequency resource. The specific information can be transmitted through a narrowband of 6 resource blocks (RBs) or 1 RB.

The above-described 5G communication technology can be combined with methods proposed in the present disclosure which will be described later and applied or can complement the methods proposed in the present disclosure to make technical features of the methods concrete and clear.

H. Voice Recognition System and AI Processing

FIG. 4 illustrates a block diagram of a schematic system in which a voice recognition method is implemented according to an embodiment of the present disclosure.

Referring to FIG. 4, a system in which a voice recognition method is implemented according to an embodiment of the present disclosure may include as a voice recognition apparatus 10, a network system 16, and a text-to-to-speech (TTS) system as a speech synthesis engine.

The at least one voice recognition device 10 may include a mobile phone 11, a PC 12, a notebook computer 13, and other server devices 14. The PC12 and notebook computer 13 may connect to at least one network system 16 via a wireless access point 15. According to an embodiment of the present disclosure, the voice recognition apparatus 10 may include an audio book and a smart speaker.

Meanwhile, the TTS system 18 may be implemented in a server included in a network, or may be implemented by on-device processing and embedded in the voice recognition device 10. In the exemplary embodiment of the present disclosure, it is assumed that the TTS system 18 is implemented in the voice recognition device 10.

FIG. 5 shows a block diagram of an AI device that may be applied to one embodiment of the present disclosure.

The AI device 20 may include an electronic device including an AI module capable of performing AI processing or a server including the AI module. In addition, the AI device 20 may be included in at least a part of the voice recognition device 10 illustrated in FIG. 4 and may be provided to perform at least some of the AI processing together.

The AI processing may include all operations related to voice recognition of the voice recognition device 10 of FIG. 6. For example, the AI processing may mean a process of analyzing a microphone detection signal of the voice recognition device 10 and detecting a voice. Further, the AI processing may mean a process of analyzing a microphone detection signal of the voice recognition device 10 to update a noise cancellation model, updating parameters of the noise cancellation model, removing an ambient noise signal of the microphone detection signal using the noise cancellation model, or obtaining an ambient noise signal from the microphone detection signal. Further, the AI processing may mean a process of obtaining data/signals related to updating of the noise cancellation model using the ambient noise signal obtained from the microphone detection signal of the voice recognition device 10.

The AI device 20 may include an AI processor 21, a memory 25, and/or a communication unit 27.

The AI device 20 is a computing device capable of learning neural networks, and may be implemented as various electronic devices such as a server, a desktop PC, a notebook PC, a tablet PC, and the like.

The AI processor 21 may learn a neural network using a program stored in the memory 25. In particular, the AI processor 21 may analyze a microphone detection signal to learn the neural network for detecting a voice from the microphone detection signal. Further, the AI processor 21 may analyze a microphone detection signal or an ambient noise signal obtained from the microphone detection signal to learn the neural network for removing the ambient noise signal from the microphone detection signal.

In this case, the neural network for recognizing a threshold gain may be designed to simulate the human's brain structure on a computer, and may include a plurality of network nodes that simulate neurons of the human's neural network and have weight.

The plurality of network nodes can transmit and receive data in accordance with each connection relationship to simulate the synaptic activity of neurons in which neurons transmit and receive signals through synapses. Here, the neural network may include a deep learning model developed from a neural network model. In the deep learning model, a plurality of network nodes is positioned in different layers and can transmit and receive data in accordance with a convolution connection relationship. The neural network, for example, includes various deep learning techniques such as deep neural networks (DNN), convolutional deep neural networks (CNN), recurrent neural networks (RNN), a restricted boltzmann machine (RBM), deep belief networks (DBN), and a deep Q-network, and can be applied to fields such as computer vision, voice recognition, natural language processing, and voice/signal processing.

Meanwhile, a processor that performs the functions described above may be a general purpose processor (e.g., a CPU), but may be an AI-only processor (e.g., a GPU) for artificial intelligence learning.

The memory 25 can store various programs and data for the operation of the AI device 20. The memory 25 may be a nonvolatile memory, a volatile memory, a flash-memory, a hard disk drive (HDD), a solid state drive (SDD), or the like. The memory 25 is accessed by the AI processor 21 and reading-out/recording/correcting/deleting/updating, etc. of data by the AI processor 21 can be performed. Further, the memory 25 can store a neural network model (e.g., a deep learning model 26) generated through a learning algorithm for data classification/recognition according to an embodiment of the present disclosure.

Meanwhile, the AI processor 21 may include a data learning unit 22 that learns a neural network for data classification/recognition. The data learning unit 22 can learn references about what learning data are used and how to classify and recognize data using the learning data in order to determine data classification/recognition. The data learning unit 22 can learn a deep learning model by obtaining learning data to be used for learning and by applying the obtained learning data to the deep learning model.

The learning data acquisition unit 23 may obtain learning data for a neural network model for classifying and recognizing data. For example, the learning data acquisition unit 23 may obtain a microphone detection signal for inputting as learning data to the neural network model, an ambient noise signal and/or a microphone detection signal, and a characteristic value extracted from the ambient noise signal.

The model learning unit 24 can perform learning such that a neural network model has a determination reference about how to classify predetermined data, using the obtained learning data. In this case, the model learning unit 24 can train a neural network model through supervised learning that uses at least some of learning data as a determination reference. Alternatively, the model learning data 24 can train a neural network model through unsupervised learning that finds out a determination reference by performing learning by itself using learning data without supervision. Further, the model learning unit 24 can train a neural network model through reinforcement learning using feedback about whether the result of situation determination according to learning is correct. Further, the model learning unit 24 can train a neural network model using a learning algorithm including error back-propagation or gradient decent.

When a neural network model is learned, the model learning unit 24 can store the learned neural network model in the memory. The model learning unit 24 may store the learned neural network model in the memory of a server connected with the AI device 20 through a wire or wireless network.

The data learning unit 22 may further include a learning data preprocessor (not shown) and a learning data selector (not shown) to improve the analysis result of a recognition model or reduce resources or time for generating a recognition model.

The learning data pre-processor may pre-process an obtained microphone detection signal/ambient noise signal so that an obtained microphone detection signal/ambient noise signal may be used for learning for removing the ambient noise signal from the microphone detection signal. For example, the learning data pre-processor may process the obtained microphone detection signal/ambient noise signal in a predetermined format so that the model learning unit 24 uses learning data obtained for learning for removing an ambient noise signal.

Further, the learning data selector can select data necessary for learning from learning data obtained by the learning data acquisition unit 23 or learning data pre-processed in the pre-processor. The selected learning data may be provided to the model learning unit 24. For example, by detecting a specific area of characteristic values of a microphone detection signal/ambient noise signal obtained from the voice recognition device 10, the learning data selection unit may select only data of syllables included in the specific area as learning data.

Further, the data learning unit 22 may further include a model estimator (not shown) to improve the analysis result of a neural network model.

The model estimator inputs estimation data to a neural network model, and when an analysis result output from the estimation data does not satisfy a predetermined reference, it can make the model learning unit 22 perform learning again. In this case, the estimation data may be data defined in advance for estimating a recognition model. For example, when the number or ratio of estimation data with an incorrect analysis result of the analysis result of a recognition model learned with respect to estimation data exceeds a predetermined threshold, the model estimator can estimate that a predetermined reference is not satisfied.

The communication unit 27 can transmit the AI processing result by the AI processor 21 to an external electronic device.

Here, the external electronic device may be defined as an autonomous vehicle. Further, the AI device 20 may be defined as another vehicle or a 5G network that communicates with the autonomous vehicle. Meanwhile, the AI device 20 may be implemented by being functionally embedded in an autonomous module included in a vehicle. Further, the 5G network may include a server or a module that performs control related to autonomous driving.

Meanwhile, the AI device 20 shown in FIG. 5 was functionally separately described into the AI processor 21, the memory 25, the communication unit 27, etc., but it should be noted that the aforementioned components may be integrated in one module and referred to as an AI module.

FIG. 6: Voice Recognition Device+AI Device

FIG. 6 is an exemplary block diagram of a voice recognition apparatus according to an embodiment of the present disclosure.

One embodiment of the present disclosure may include computer readable and computer executable instructions that may be included in the voice recognition apparatus 10. Although FIG. 6 discloses a plurality of components included in the voice recognition apparatus 10, the components not disclosed may be included in the voice recognition apparatus 10.

A plurality of voice recognition apparatuses may be applied to one voice recognition apparatus. In such a multi-device system the voice recognition apparatus may comprise different components for performing various aspects of voice recognition processing. The voice recognition apparatus 10 shown in FIG. 6 is exemplary and may be an independent apparatus or may be implemented as a component of a larger apparatus or system.

One embodiment of the present disclosure may be applied to a plurality of different devices and computer systems, for example, a general purpose computing system, a server-client computing system, a telephone computing system, a laptop computer, a portable terminal, a PDA, a tablet computer, and the like. The voice recognition device 10 may also be applied to one component of another device or system that provides voice recognition such as automated teller machines (ATMs), kiosks, global positioning systems (GPS), home appliances (eg, refrigerators, ovens, washing machines, etc.), vehicles (vehicles), e-book readers.

As shown in FIG. 6, the voice recognition apparatus 10 includes a communication unit 110, an input unit 120, an output unit 130, a memory 140, a sensing unit 150, an interface unit 160, and a power supply unit 190 and/or processor 170. On the other hand, some of the components disclosed in the voice recognition apparatus 10 may appear as a single component several times in one device.

The voice recognition apparatus 10 may include an address/data bus (not shown) for transferring data between the components of the voice recognition apparatus 10. Each component in the voice recognition apparatus 10 may be directly connected to other components through the bus (not shown). Meanwhile, each component in the voice recognition apparatus 10 may be directly connected to the processor 170.

More specifically, the communication unit 110 may include one or more modules that enable communication between the above components, wireless communication between the electronic device 10 and the wireless communication system, between the electronic device 10 and another electronic device, or between the electronic device 10 and an external server. In addition, the communication unit 110 may include one or more modules for connecting the electronic device 10 to one or more networks.

The communication unit 110 may be a wireless communication device such as a radio frequency (RF), an infrared (Infrared), Bluetooth, a wireless local area network (WLAN) (Wi-Fi, etc.) or 5G network, a Long Term Evolution (LTE) network, wireless network wireless devices such as WiMAN networks, 3G networks.

The communication unit 110 may include at least one of a broadcast receiving module, a mobile communication module, a wireless internet module, a short range communication module, and a location information module.

The input unit 120 may include a microphone, a touch input unit, a keyboard, a mouse, a stylus, or another input unit.

In addition, the input unit 120 may include a camera or an image input unit for inputting an image signal, a microphone or an audio input unit for inputting an audio signal, and a user input unit (eg, a touch key, push key (mechanical key, etc.)) for receiving information from a user. The voice data or the image data collected by the input unit 120 may be analyzed and processed as a control command of the user.

The sensing unit 150 may include one or more sensors for sensing at least one of information in the electronic device 10, surrounding environment information surrounding the electronic device 10, and user information.

For example, the sensing unit 150 may include at least one of a proximity sensor, an illumination sensor, a touch sensor, an acceleration sensor, a magnetic sensor, and a gravity sensor (G-sensor), gyroscope sensor, motion sensor, RGB sensor, infrared sensor (IR sensor), fingerprint scan sensor, ultrasonic sensor, optical sensor (e.g., imaging means), microphones, battery gauges, environmental sensors (e.g. barometers, hygrometers, thermometers, radiation sensors, heat sensors, gas sensors, etc.), a chemical sensor (eg, electronic nose, healthcare sensor, biometric sensor, etc.). Meanwhile, the electronic device 10 disclosed herein may use a combination of information sensed by at least two or more of these sensors.

The output unit 130 may output information (for example, voice) processed by the voice recognition device 10 or another device. The output unit 130 may include a speaker, a headphone, or other suitable component for propagating voice. As another example, the output unit 130 may include an audio output unit. In addition, the output unit 130 may include a display (visual display or tactile display), audio speakers, headphones, printer or other output unit. The output unit 130 may be integrated into the voice recognition apparatus 10 or may be implemented separately from the voice recognition apparatus 10.

The output unit 130 is used to generate an output related to visual, auditory or tactile, and may include at least one of a display unit, an audio output unit, a hap tip module, and an optical output unit. The display unit may form a layer structure or an integrated structure with the touch sensor, thereby implementing a touch screen. Such a touch screen may serve as a user input means for providing an input interface between the augmented reality electronic device 10 and the user, and at the same time, provide an output interface between the augmented reality electronic device 10 and the user.

Input 120 and/or output 130 may also include an interface for connecting external peripherals such as Universal Serial Bus (USB), FireWire, Thunderbolt or other connection protocols. Input 120 and/or output 130 may also include a network connection, such as an Ethernet port, modem, or the like. The voice recognition apparatus 10 may be connected to the Internet or a distributed computing environment through the input unit 120 and/or the output unit 130. In addition, the voice recognition apparatus 10 may be connected to a removable or external memory (eg, a removable memory card, a memory key drive, a network storage, etc.) through the input unit 120 or the output unit 130.

The interface unit 160 serves as a path to various types of external devices connected to the electronic device 10. The electronic device 10 may receive virtual reality or augmented reality content from an external device through the interface unit 160, and may interact with each other by exchanging various input signals, sensing signals, and data.

For example, the interface unit 160 may include a device equipped with a wired/wireless headset port, an external charger port, a wired/wireless data port, a memory card port, and an identification module. It may include at least one of a port connecting, an audio input/output (I/O) port, a video input/output (I/O) port, and an earphone port. The memory 140 may store data and instructions. The memory 140 may include a magnetic storage, an optical storage, a solid-state storage type, and the like.

The memory 140 may include volatile RAM, nonvolatile ROM, or another type of memory.

In addition, the memory 140 stores data supporting various functions of the electronic device 10. The memory 140 may store a plurality of application programs or applications that are driven in the electronic device 10, data for operating the electronic device 10, and instructions. At least some of these applications may be downloaded from an external server via wireless communication. At least some of these application programs may be present on the electronic device 10 from the time of shipment for the basic functions of the electronic device 10 (for example, a call forwarding, a calling function, a message receiving, and a calling function).

The voice recognition apparatus 10 may include a processor 170. The processor 170 may be connected to a bus (not shown), an input unit 120, an output unit 130, and/or other components of the voice recognition device 10. The processor 170 may correspond to a CPU for processing data, a computer readable instruction for processing data, and a memory for storing data and instructions.

In addition to the operations associated with the application, the processor 170 also typically controls the overall operation of the electronic device 10. The processor 170 may process signals, data, information, and the like, which are input or output through the above-described components.

In addition, the processor 170 may control at least some of the components by driving an application program stored in the memory 140 to provide appropriate information to the user or to process a function. In addition, the processor 170 may operate at least two or more of the components included in the electronic device 10 in combination with each other to drive an application program.

In addition, the processor 170 may detect the movement of the electronic device 10 or the user by using a gyroscope sensor, a gravity sensor, a motion sensor, or the like included in the sensing unit 150. Alternatively, the processor 170 may detect an object approaching to the electronic device 10 or the user by using a proximity sensor, an illumination sensor, a magnetic sensor, an infrared sensor, an ultrasonic sensor, an optical sensor, etc. included in the sensing unit 150. In addition, the processor 170 may detect a user's movement through sensors provided in a controller that operates in conjunction with the electronic device 10.

In addition, the processor 170 may perform an operation (or function) of the electronic device 10 using an application program stored in the memory 140.

Computer instructions to be processed in the processor 170 for operating the voice recognition apparatus 10 and various components may be executed by the processor 170 and may include a memory 140, an external device, or a processor to be described later. It may be stored in the memory or storage included in (170). Alternatively, all or some of the executable instructions may be embedded in hardware or firmware in addition to software. One embodiment of the disclosure may be implemented in various combinations of software, firmware and/or hardware, for example.

In detail, the processor 170 may process the text data into an audio waveform including voice, or may process the audio waveform into text data. The source of the textual data may be generated by an internal component of the voice recognition apparatus 10. In addition, the source of the text data may be received from the input unit such as a keyboard or transmitted to the voice recognition apparatus 10 through a network connection. The text may be in the form of sentences that include text, numbers, and/or punctuation for conversion by the processor 170 into speech. The input text may also include a special annotation for processing by the processor 170, and may indicate how the specific text should be pronounced through the special annotation. The text data may be processed in real time or later stored and processed.

In addition, although not shown in FIG. 6, the processor 170 may include a front end, a speech synthesis engine, and a TTS storage. The preprocessor may convert the input test data into a symbolic linguistic representation for processing by the speech synthesis engine. The speech synthesis engine may convert the input text into speech by comparing annotated phonetic units models with information stored in the TTS storage. The preprocessor and the speech synthesis engine may include an embedded internal processor or memory, or may use the processor 170 and the memory 140 included in the voice recognition apparatus 10. Instructions for operating the preprocessor and the speech synthesis engine may be included in the processor 170, the memory 140 of the voice recognition apparatus 10, or an external device.

Text input to the processor 170 may be sent to the preprocessor for processing. The preprocessor may include a module for performing text normalization, linguistic analysis, and linguistic prosody generation.

During the text normalization operation, the preprocessor processes the text input and generates standard text, converting numbers, abbreviations, and symbols the same as they are written.

During the language analysis operation, the preprocessor may analyze the language of the normalized text to generate a series of phonetic units corresponding to the input text. This process may be called phonetic transcription.

Phonetic units are finally combined to include symbolic representations of sound units output by the voice recognition device 10 as speech. Various sound units can be used to segment text for speech synthesis.

Processor 170 includes phonemes (individual sounds), half-phonemes, di-phones (the last half of a phoneme combined with the first half of adjacent phonemes) and bi-phones. Speech can be processed based on two successive sonic speeds, syllables, words, phrases, sentences, or other units. Each word may be mapped to one or more phonetic units. Such mapping may be performed using a language dictionary stored in the voice recognition apparatus 10.

Linguistic analysis performed by the preprocessor may also involve identifying different grammatical elements, such as prefixes, suffixes, phrases, punctuation, and syntactic boundaries. Such grammatical components can be used by the processor 170 to produce natural audio waveform output. The language dictionary may also include letter-to-sound rules and other tools that may be used to pronounce previously unverified words or letter combinations that may be generated by the processor 170. In general, the more information included in the language dictionary, the higher the quality of voice output can be guaranteed.

Based on the language analysis, the preprocessor may perform language rhythm generation annotated to phonetic units with prosodic characteristics indicating how the final sound unit should be pronounced in the final output speech.

The rhyme characteristics may also be referred to as acoustic features. During the operation of this step, the preprocessor may integrate into the processor 170 taking into account any prosodic annotations involving text input. Such acoustic features may include pitch, energy, duration, and the like. Application of the acoustic feature may be based on prosodic models available to the processor 170.

This rhyme model represents how phonetic units should be pronounced in certain situations. For example, a rhyme model can include a phoneme's position in a syllable, a syllable's position in a word, or a word's position in a sentence or phrase, phrases neighboring phonetic units, and the like. Like the language dictionary, the more information of the prosodic model, the higher the quality of voice output can be guaranteed.

The output of the preprocessor may include a series of speech units annotated with prosodic characteristics. The output of the preprocessor may be referred to as a symbolic linguistic representation. The symbolic language representation may be sent to a speech synthesis engine.

The speech synthesis engine performs a process of converting a speech into an audio waveform for output to the user through the output unit 130. The speech synthesis engine may be configured to convert the input text into high quality natural speech in an efficient manner. Such high quality speech can be configured to pronounce as much as possible a human speaker.

The speech synthesis engine may perform speech synthesis using at least one other method.

The Unit Selection Engine contrasts a recorded speech database with a symbolic linguistic representation generated by the preprocessor. The unit selection engine matches the symbol language representation with the speech audio unit of the speech database. Matching units can be selected to form a speech output and the selected matching units can be connected together. Each unit has only an audio waveform corresponding to a phonetic unit, such as a short .wav file of a particular sound, with a description of the various acoustic characteristics associated with the .wav file (pitch, energy, etc.). In addition, the speech unit may include other information such as a word, a sentence or a phrase, a location displayed on a neighboring speech unit.

The unit selection engine can match the input text using all the information in the unit database to produce a natural waveform. The unit database may include an example of a number of speech units that provide different options to the voice recognition device 10 for connecting the units in speech. One of the advantages of unit selection is that natural voice output can be produced depending on the size of the database. In addition, as the unit database is larger, the voice recognition apparatus 10 may configure natural speech.

On the other hand, there is a parameter synthesis method in addition to the above-described unit selection synthesis. Parametric synthesis allows synthesis parameters such as frequency, volume and noise to be modified by a parametric synthesis engine, a digital signal processor, or other audio generating device to produce an artificial speech waveform.

Parametric synthesis can be matched to a desired linguistic representation desired output speech parameter using an acoustic model and various statistical techniques. Parameter synthesis not only processes speech without the large database associated with unit selection, but also enables accurate processing at high processing speeds. The unit selection synthesis method and the parameter synthesis method may be performed separately or in combination to generate a voice audio output.

Parametric speech synthesis may be performed as follows. The processor 170 may include an acoustic model capable of converting a symbolic linguistic representation into a synthetic acoustic waveform of the text input based on the audio signal manipulation. The acoustic model may include rules that may be used by the parameter synthesis engine to assign specific audio waveform parameters to input speech units and/or prosodic annotations. The rule can be used to calculate a score indicating the likelihood that a particular audio output parameter (frequency, volume, etc.) corresponds to the portion of the input symbolic language representation from the preprocessor.

The parametric synthesis engine may apply a plurality of techniques to match the voice to be synthesized with the input speech unit and/or the rhyme annotation. One common technique uses the Hidden Markov Model (HMM), which can be used to determine the probability that an audio output should match text input. The HMM can be used to convert the parameters of language and acoustic space into parameters to be used by a vocoder (digital voice encoder) to artificially synthesize the desired speech.

The voice recognition apparatus 10 may also include a speech unit database for use in unit selection. The voice unit database may be stored in memory 140 or other storage configuration. The voice unit database may include recorded speech utterance. The speech utterance may be text corresponding to the speech content. In addition, the speech unit database may include recorded speech (in the form of audio waveforms, feature vectors or other formats) that takes up significant storage space in the voice recognition device 10. Unit samples of the speech unit database may be classified in a variety of ways, including speech units (phonemes, diphonies, words, etc.), linguistic rhyme labels, sound feature sequences, speaker identities, and the like. Sample utterance can be used to generate a mathematical model corresponding to the desired audio output for a particular speech unit.

When the speech synthesis engine matches the symbolic language representation, it may select a unit in the speech unit database that most closely matches the input text (including both speech units and rhythm annotations). In general, the larger the voice unit database, the greater the number of selectable unit samples, thus enabling accurate speech output.

The processor 170 may transmit audio waveforms including audio output to the output unit 130 for output to the user. The processor 170 may store the audio waveform including speech in the memory 140 in a plurality of different formats, such as a series of feature vectors, uncompressed audio data, or compressed audio data. For example, processor 170 may encode and/or compress voice output using an encoder/decoder prior to the transmission. The encoder/decoder may encode and decode audio data such as digitized audio data, feature vectors, and the like. In addition, the functions of the encoder/decoder may be located in separate components or may be performed by the processor 170.

The memory 140 may store other information for voice recognition. The contents of memory 140 may be prepared for general voice recognition and TTS use, and may be customized to include sounds and words that are likely to be used in a particular application. For example, the TTS storage may include customized voice specialized for location and navigation for TTS processing by the GPS device.

The memory 140 may also be customized to the user based on the personalized desired voice output. For example, a user may prefer a voice that is output to a specific gender, a specific intonation, a specific speed, a specific emotion (eg, a happy voice). The speech synthesis engine may include a specialized database or model to describe such user preferences.

The voice recognition apparatus 10 may also be configured to perform TTS processing in multiple languages. For each language, processor 170 may include data, instructions, and/or components specifically configured to synthesize speech in a desired language.

The processor 170 may modify or update the contents of the memory 140 based on the feedback on the result of the TTS processing to improve performance, so that the processor 170 may improve awareness of the voice more than the capability provided by the training corpus.

As the processing power of the voice recognition apparatus 10 is improved, the speech output may be performed by reflecting the emotion attribute of the input text. Alternatively, even if the input text is not included in the emotion attribute, the voice recognition apparatus 10 may output the voice by reflecting the intention (emotional information) of the user who created the input text.

Indeed, when a model is built that will be integrated into a TTS module that performs TTS processing, the TTS system may integrate the various components mentioned above with other components. For example, the voice recognition apparatus 10 may include a block for setting a speaker.

The speaker setting unit may set the speaker for each character appearing in the script. The speaker setup unit may be integrated into the processor 170 or may be integrated as part of the preprocessor or speech synthesis engine. The speaker setting unit synthesizes text corresponding to a plurality of characters into a set speaker's voice using metadata corresponding to a speaker profile.

According to an embodiment of the present disclosure, a markup language may be used as the meta data, and preferably, speech synthesis markup language (SSML) may be used.

The power supply unit 190 receives power from an external power source or an internal power source under the control of the processor 170 to supply power to each component included in the electronic device 10. The power supply unit 190 includes a battery, and the battery may be provided in a built-in or replaceable form.

Hereinafter, a speech processing procedure performed by a device environment and/or a cloud environment or server environment will be described with reference to FIGS. 7 and 8. FIG. 7 shows an example in which, while a speech can be received in a device 50, a procedure of processing the received speech and thereby synthesize the speech, that is, overall operations of speech synthesis, is performed in a cloud environment 60. On the contrary, FIG. 8 shows an example of on-device processing indicating that a device 70 performs the aforementioned overall operations of speech synthesis by processing a received speech and thereby synthesizing the speech.

In FIGS. 7 and 8, the device environments 70 may be referred to as client devices, and the cloud environments 60 and 80 may be referred to as servers.

FIG. 7 shows a schematic block diagram of a text-to-speech (TTS) device in a TTS system according to an embodiment of the present disclosure.

In order to process a speech event in an end-to-end speech UI environment, various configurations are required. A sequence for processing the speech event performs signal acquisition playback, speech pre-processing, voice activation, voice recognition, natural language processing, and speech synthesis by which a device responds to a user.

The client device 50 may include an input module. The input module may receive a user input from a user. For example, the input module may receive the user input from an external device (e.g., a keyboard and a headset) connected thereto. In addition, for example, the input module may include a touch screen. In addition, for example, the input module may include a hardware key located in a user terminal.

According to an embodiment, the input module may include at least one microphone capable of receiving a user's utterance as a speech signal. The input module may include a speech input system and receive a user's speech as a speech signal through the speech input system. By generating an input signal for an audio input, the at least one microphone may determine a digital input signal for a user's speech. According to an embodiment, multiple microphones may be implemented as an array. The array may be arranged in a geometric pattern, for example, a linear geometric shape, a circular geometric shape, or a different random shape. For example, the array may be in a pattern in which four sensors are placed at 90 degrees to receive sound from four directions. In some embodiments, the microphone may include sensors of different arrays in a space of data communication, and may include a networked array of the sensors. The microphone may include an omnidirectional microphone and a directional microphone (e.g., a shotgun microphone).

The client device 50 may include a pre-processing module 51 capable of pre-processing a user input (speech signal) that is received through the input module (e.g., a microphone).

The pre-processing module 51 may include an adaptive echo canceller (AEC) function to thereby remove echo included in a user speech signal received through the microphone. The pre-processing module 51 may include a noise suppression (NS) function to thereby remove background noise included in a user input. The pre-processing module 51 may include an end-point detect (EPD) function to thereby detect an end point of a user speech and thus find out where the user speech exists. In addition, the pre-processing module 51 may include an automatic gain control (AGC) function to thereby control volume of the user speech in such a way suitable for recognizing and processing the user speech.

The client device 50 may include a voice activation module 52. The voice activation module 52 may recognize a wake-up call indicative of recognition of a user's call. The voice activation module 52 may detect a predetermined keyword (e.g., Hi LG) from a user input which has been pre-processed. The voice activation module 52 may remain in a standby state to perform an always-on keyword detection function.

The client device 50 may transmit a user voice input to a cloud server. ASR and natural language understanding (NLU) operations, which are essential to process a user speech, is generally performed in Cloud due to computing, storage, power limitations, and the like. The Cloud may include the cloud device 60 that processes a user input transmitted from a client. The cloud device 60 may exists as a server.

The cloud device 60 may include an auto voice recognition (ASR) module 61, an artificial intelligent agent 62, a natural language understanding (NLU) module 63, a text-to-speech (TTS) module 64, and a service manager 65.

The ASR module 61 may convert a user input, received from the client device 50, into textual data.

The ASR module 61 includes a front-end speech pre-processor. The front-end speech pre-processor extracts a representative feature from a speech input. For example, the front-perform a Fourier transform on the speech input to extract spectral features that characterize the speech input as a sequence of representative multi-dimensional vectors. In addition, The ASR module 61 may include one or more voice recognition modules (e.g., an acoustic model and/or a language module) and may realize one or more voice recognition engines. Examples of the voice recognition model include Hidden Markov Models, Gaussian-Mixture Models, Deep Neural Network Models, n-gram language models, and other statistical models. Examples of the voice recognition model include a dynamic time warping (DTW)-based engine and a weighted finite state transducer (WFST)-based engine. The one or more voice recognition models and the one or more voice recognition engines can be used to process the extracted representative features of the front-end speech pre-processor to produce intermediate recognitions results (e.g., phonemes, phonemic strings, and sub-words), and ultimately, text recognition results (e.g., words, word strings, or sequence of tokens).

Once the ASR module 61 generates a recognition result including a text string (e.g., words, or sequence of words, or sequence of tokens), the recognition result is transmitted to the NLP module 732 for intention deduction. In some examples, The ASR module 730 generates multiple candidate text expressions for a speech input. Each candidate text expression is a sequence of works or tokens corresponding to the speech input.

The NLU module 63 may perform a syntactic analysis or a semantic analysis to determine intent of a user. The syntactic analysis may be used to divide a user input into syntactic units (e.g., words, phrases, morphemes, or the like) and determine whether each divided unit has any syntactic element. The semantic analysis may be performed using semantic matching, rule matching, formula matching, or the like. Thus, the NLU module 63 may obtain a domain, intent, or a parameter (or a slot) necessary to express the intent from a user input through the above-mentioned analysis.

According to an embodiment, the NLU module 63 may determine the intent of the user and a parameter using a matching rule which is divided into a domain, intent, and a parameter. For example, one domain (e.g., an alarm) may include a plurality of intents (e.g., alarm setting, alarm release, and the like), and one intent may need a plurality of parameters (e.g., a time, the number of iterations, an alarm sound, and the like). The plurality of rules may include, for example, one or more mandatory parameters. The matching rule may be stored in a natural language understanding database.

According to an embodiment, the NLU module 63 may determine a meaning of a word extracted from a user input using a linguistic feature (e.g., a syntactic element) such as a morpheme or a phrase and may match the determined meaning of the word to the domain and intent to determine the intent of the user. For example, the NLU module 63 may determine the intent of the user by calculating how many words extracted from a user input are included in each of the domain and the intent. According to an embodiment, the NLU module 63 may determine a parameter of the user input using a word which is the basis for determining the intent. According to an embodiment, the NLU module 63 may determine the intent of the user using a NLU DB which stores the linguistic feature for determining the intent of the user input. According to another embodiment, the NLU module 63 may determine the intent of the user using a personal language model (PLM). For example, the NLU module 63 may determine the intent of the user using personalized information (e.g., a contact list, a music list, schedule information, social network information, etc.). For example, the PLM may be stored in, for example, the NLU DB. According to an embodiment, the ASR module 61 as well as the NLU module 63 may recognize a voice of the user with reference to the PLM stored in the NLU DB.

According to an embodiment, the NLU module 63 may further include a natural language generating module (not shown). The natural language generating module may change specified information to a text form. The information changed to the text form may be a natural language speech. For example, the specified information may be information about an additional input, information for guiding the completion of an action corresponding to the user input, or information for guiding the additional input of the user. The information changed to the text form may be displayed in a display after being transmitted to the client device or may be changed to a voice form after being transmitted to the TTS module.

The TTS module 64 may convert text input to voice output. The TTS module 64 may receive text input from the NLU module 63 of the LNU module 63, may change the text input to information in a voice form, and may transmit the information in the voice form to the client device 50. The client device 50 may output the information in the voice form via the speaker.

The speech synthesis module 64 synthesizes speech outputs based on a provided text. For example, a result generated by the ASR module 61 may be in the form of a text string. The speech synthesis module 64 may convert the text string to an audible speech output. The speech synthesis module 64 may use any appropriate speech synthesis technique in order to generate speech outputs from text, including, but not limited, to concatenative synthesis, unit selection synthesis, diphone synthesis, domain-specific synthesis, formant synthesis, articulatory synthesis, hidden Markov model (HMM) based synthesis, and sinewave synthesis.

In some examples, the speech synthesis module 64 may be configured to synthesize individual words based on phonemic strings corresponding to the words. For example, a phonemic string can be associated with a word in a generated text string. The phonemic string can be stored in metadata associated with the word. The speech synthesis model 64 may be configured to directly process the phonemic string in the metadata to synthesize the word in speech form.

Since the cloud environment generally has more processing capabilities or resources than the client device, a higher quality speech output may be obtained in synthesis on the client side. However, the present disclosure is not limited thereto, and the speech synthesis process may be performed on the client side (see FIG. 8).

Meanwhile, according to an embodiment, the client environment may further include an Artificial Intelligence (AI) agent 62. The AI processor 62 is defined to perform at least some of the above-described functions performed by the ASR module 61, the NLU module 62 and/or the TTS module 64. In addition, the AI module 62 may make contribution so that the ASR module 61, the NLU module 62 and/or the TTS module 64 perform independent functions, respectively.

The AI processor module 62 may perform the above-described functions through deep learning. The deep learning represents a certain data in a form readable by a computer (e.g., when the data is an image, pixel information is represented as column vectors or the like), and efforts are being made to conduct enormous researches for applying the representation to learning (which is about how to create better representation techniques and how to create a model that learns the better representation techniques), and, as a result, various deep learning techniques such as deep neural networks (DNN), convolutional deep neural networks (CNN), Recurrent Boltzmann Machine (RNN), Restricted Boltzmann Machine (RBM), deep belief networks (DBN), and Deep Q-Network, may be applied to computer vision, voice recognition, natural language processing, speech/signal processing, and the like.

Currently, all commercial voice recognition systems (Microsoft's Cortana, Skype translator, Google Now, Apple Siri, etc.). are based on deep learning techniques.

In particular, the AI processor module 62 may perform various natural language processes, including machine translation, emotion analysis, and information retrieval, to process natural language by use of a deep artificial neural network architecture.

Meanwhile, the cloud environment may include a service manager 65 capable of collecting various personalized information and supporting a function of the AI processor 62. The personalized information obtained through the service manager may include at least one data (a calendar application, a messaging service, usage of a music application, etc.) used through the cloud environment, at least one sensing data (a camera, a microphone, temperature, humidity, a gyro sensor, C-V2X, a pulse, ambient light, Iris scan, etc.) collected by the client device 50 and/or the cloud 60, off device data directly not related to the client device 50. For example, the personalized information may include maps, SMS, news, music, stock, weather, Wikipedia information.

For convenience of explanation, the AI processor 62 is represented as an additional block to be distinguishable from the ASR module 61, the NLU module 63, and the TTS module 64, but the AI processor 62 may perform at least some or all of the functions of the respective modules 61, 62, and 64.

In FIG. 7, an example in which the AI processor 62 is implemented in the cloud environment due to computing calculation, storage, power limitations, and the like, but the present disclosure is not limited thereto.

For example, FIG. 8 is identical to what is shown in FIG. 7, except for a case where the AI processor is included in the cloud device.

FIG. 8 is a schematic block diagram of a TTS device in a TTS system environment according to an embodiment of the present disclosure. A client device 70 and a cloud environment 80 shown in FIG. 8 may correspond to the client device 50 and the cloud device 60 aforementioned in FIG. 7, except for some configurations and functions. Accordingly, description about specific functions of corresponding blocks may refer to FIG. 7.

Referring to FIG. 8, the client device 70 may include a pre-processing module 51, a voice activation module 72, an ASR module 73, an AI processor 74, an NLU module 75, and a TTS module 76. In addition, the client device 50 may include an input module (at least one microphone) and at least one output module.

In addition, the cloud environment may include cloud knowledge 80 that stores personalized information in a knowledge form.

A function of each module shown in FIG. 8 may refer to FIG. 7. However, since the ASR module 73, the NLU module 75, and the TTS module 76 are included in the client device 70, communication with Cloud may not be necessary for a speech processing procedure such as voice recognition, speech synthesis, and the like, and thus, an instant real-time speech processing operation is possible.

Each module shown in FIGS. 7 and 8 are merely an example for explaining a speech processing procedure, and modules more or less than in FIGS. 7 and 8 may be included. In addition, two or more modules may be combined or different modules or modules with different arrangement structures may be included. The various modules shown in FIGS. 7 and 8 may be implemented in hardware, software instructions for execution by one or more processors, firmware, including one or more signal processing and/or application specific integrated circuits, or a combination thereof.

FIG. 9 is a schematic block diagram of an AI processor capable of performing emotion classification information-based TTS according to an embodiment of the present disclosure.

Referring to FIG. 9, in the speech processing procedure described with reference to FIGS. 7 and 8, the AI processor 74 may support an interactive operation with a user, in addition to an ASR operation, an NLU operation, and a TTS operation. Alternatively, using context information, the AI processor 74 may make contribution so that the NLU module 63 further clarify, complements, or additionally define information included in text expressions received from the ASR module 61.

Here, the context information may include preference of a user of a client device, hardware and/or software states of the client device, various types of sensor information received before, during, or after a user input, previous interactions (e.g., dialogue) between the AI processor and the user, etc. In the present disclosure, the context information is dynamic and varies depending on time, location, contents of the dialogue, and other elements.

The AI processor 74 may further include a context fusion and learning module 91, a local knowledge 92, and a dialogue management 93.

The context fusion and learning module 91 may learn a user's intent based on at least one data. The at least one data may further include at least one sensing data obtained by a client device or a cloud environment. In addition, the at least one data may further include speaker identification, acoustic event detection, a speaker's personal information (gender and age detection), voice activity detection (VAD), and emotion classification information.

The speaker identification may indicate specifying a speaker in a speaker group registered by a speech. The speaker identification may include identifying a pre-registered speaker or registering a new speaker. The acoustic event detection may outdo a voice recognition technique and may be used to recognize acoustics itself to recognize a type of sound and a place where the sound occurs. The VAD is a speech processing technique of detecting presence or absence of a human speech (voice) from an audio signal that can include music, noise, or any other sound. According to an embodiment, the AI processor 74 may detect presence of a speech from the input audio signal. According to an embodiment the AI processor 74 differentiates a speech data and a non-speech data using a deep neural networks (DNN) model. In addition, the AI processor 74 may perform emotion classification information on the speech data using the DNN model. According to the emotion classification information, the speech data may be classified as anger, boredom, fear, happiness, or sadness.

The contest fusion and learning module 91 may include a DNN model to perform the above-described operation, and may determine intent of a user input based on sensing information collected in the DNN model, the client device or the cloud environment.

The at least one data is merely an example and may include any data that can be referred to so as to determine intent of a user in a speech processing procedure. The at least one data may be obtained through the above-described DNN model.

The AI processor 74 may include the local knowledge 92. The local knowledge 92 may include user data. The user data may include a user's preference, the user's address, the user's initially set language, the user's contact list, etc. According to an embodiment, the AI processor 74 may additionally define the user's intent by complementing information included in the user's speech input using the user's specific information. For example, in response to the user's request “Invite my friends to my birthday party”, the AI processor 74 does not request more clarified information from the user and may utilize the local knowledge 92 to determine who “the friends” are and when and where the “birthday” takes place.

The AI processor 74 may further include the dialogue management 93. The AI processor 74 may provide a dialogue interface to enable speech conversation with the user. The dialogue interface may refer to a procedure of outputting a response to the user's speech input through a display or a speaker. Here, a final result output through the dialogue interface may be based on the ASR operation, the NLU operation, and the TTS operation, which are described above.

I. Specific Operation of the Present Disclosure

Description of FIG. 10

FIG. 10 is a flowchart illustrating a voice recognition method according to an embodiment of the present disclosure.

As illustrated in FIG. 10, a method of recognizing a voice (S100) according to an embodiment of the present disclosure may include steps S110, S120, S130, S140, and S150 and a detailed description thereof is as follows.

First, the voice recognition device 10 described with reference to FIGS. 4 to 9 may recognize a wakeup word and obtain a microphone detection signal, which is a signal detected by at least one microphone (the input unit 120) of the voice recognition device (S110).

Here, before step S110, the voice recognition device 10 may recognize that a wakeup word is included in the user's voice using the voice recognition activation modules 52 and 72 of FIGS. 7 and 8. When a wakeup word is recognized, the voice recognition device 10 may activate the ASR modules 61 and 73 or the AI processors 62 and 74 of FIGS. 7 and 8 and recognize the user's voice using the ASR modules 61 and 73 or the AI processors 62 and 74.

A microphone detection signal detected by at least one microphone 120 of the voice recognition device may include a user's voice signal including a user's wakeup word and/or continuous word. Further, a microphone detection signal detected by at least one microphone 120 of the voice recognition device may include an ambient noise signal of at least one microphone 120 of the voice recognition device as well as a user's voice signal including a user's wakeup word and/or continuous word. That is, a microphone detection signal detected by at least one microphone 120 of the voice recognition device may be a mixed signal of an ambient noise signal and a user's voice signal including a user's wakeup word and/or continuous word.

Here, the processor 170 of the voice recognition device 10 may perform a function of the voice recognition device 10. That is, the processor 170 may obtain a microphone detection signal, which is a mixed signal of an ambient noise signal and a user's voice signal including a user's wakeup word and/or continuous word detected by at least one microphone 120 of the voice recognition device.

Thereafter, the processor 170 of the voice recognition device 10 may determine whether the user's voice is detected from the obtained microphone detection signal (S120).

For example, the processor 170 may determine whether the user's voice is detected from the microphone detection signal using a voice detection model embedded therein or stored in the memory 140. Here, the processor 170 may control the ASR modules 61 and 73 of FIGS. 7 and 8 or the AI processors 62 and 74 of FIGS. 7 and 8 to determine whether the user's voice is detected from the microphone detection signal.

If the users voice is not detected from the microphone detection signal, the processor 170 may obtain a new microphone detection signal (S110) and repeat step S120 until a voice is detected from the new microphone detection signal.

When the user's voice is detected from the microphone detection signal, the processor 170 may obtain an ambient noise signal from the microphone detection signal (S130).

Here, the processor 170 may divide an entire segment in a time domain of the microphone detection signal into a voice segment, which is a time segment in which a user's voice is detected and a non-voice segment, which is a time segment in which a user's voice is not detected. For example, the processor 170 may recognize a microphone detection signal of a non-voice segment, which is a time segment in which a user's voice is not detected as an ambient noise signal instead of a voice segment, which is a time segment in which a user's voice is detected. That is, the processor 170 may obtain a signal of the non-voice segment among an entire segment of the microphone detection signal as an ambient noise signal.

Thereafter, the processor 170 may update the previously learned noise cancellation model based on the ambient noise signal (S140).

For example, the previously learned noise cancellation model may be a previously learned noise cancellation model based on other noise signals obtained in a predetermined space different from a space in which the voice recognition device 10 is currently located and another voice signal of a predetermined another user. The previously learned noise cancellation model may be previously learned after the voice recognition device 10 is produced before step S110.

Here, the memory 140 may store another voice signal used for learning the previously learned noise cancellation model. For example, the processor 170 may update the previously learned noise cancellation model using other voice signal stored in the memory 140 and a currently obtained ambient noise signal. The updating process may be referred to as transfer learning. That is, the processor 170 may perform transfer learning (updating) of the previously learned noise cancellation model using other voice signal stored in the memory 140 before step S110 and the ambient noise signal obtained at step S130.

For example, the voice recognition device 10 may update parameters of an existing previously learned noise cancellation model using an ambient noise signal obtained in a current location space and predetermined other voice.

Specifically, by applying previously generated parameters (coefficient or weight) to the input value, the noise cancellation model may output an output value. That is, the voice recognition device 10 may set an ambient noise signal obtained in a current location space and previously learned another voice as an input value and set predetermined another voice to an output value to change (update) previously generated parameters, thereby updating the noise cancellation model.

Finally, the processor 170 may remove an ambient noise signal from the microphone detection signal using the updated (transfer learned) noise cancellation model (S150).

For example, the processor 170 may remove an ambient noise signal from a voice segment and non-voice segment of the microphone detection signal using the updated noise cancellation model. For another example, the processor 170 may remove an ambient noise signal from a voice segment of the microphone detection signal using the updated noise cancellation model.

Although not illustrated in FIG. 10, after step S150, the processor 170 may output a microphone detection signal in which an ambient noise signal is removed through at least one speaker (e.g., the output unit 130 of FIG. 6).

Description of FIG. 11

FIG. 11 is a diagram illustrating a process in which a voice recognition device obtains an ambient noise signal.

As illustrated in FIG. 11, the voice recognition device 10 may detect sound signals of all forms of a voice recognition system 1100 through a microphone and define a sound signal of all forms detected through the microphone to a microphone detection signal.

Specifically, the voice recognition device 10 may obtain a user's wakeup word (“Hi, LG”) 1101 as a microphone detection signal. Further, the voice recognition device 10 may obtain a specific sound signal reflected from an object (outer wall) on a space where the voice recognition device 10 is located or ambient noise signals 1111, 1112, 1113, and 1114 in which a specific sound signal is directly transferred and detected as a microphone detection signal.

For example, the ambient noise signals may include noise signals 1111 and 1112 directly transferred to the voice recognition device 10 and reverberation signals 1113 and 1114 reflected and detected from the object.

However, because the voice recognition device 10 recognizes both the noise signal and the reverberation signal as ambient noise signals, and in this specification, the noise signal and the reverberation signal are all defined as ‘ambient noise signal’.

Description of FIG. 12

FIG. 12 is a diagram illustrating a process in which a voice recognition device performs transfer learning (updating).

As illustrated in FIG. 12, before step S110 of FIG. 10, the voice recognition device 10 may generate (or previously learn) a noise cancellation model 1241 using another voice (e.g., laboratory voice) and another noise (e.g., laboratory noise) of a predetermined another space (e.g., laboratory) 1201.

Here, the previously learned noise cancellation model 1241 may be defined to an existing noise cancellation model using another voice and another noise in the predetermined another space 1201.

After the existing noise cancellation model is previously learned (or generated), the voice recognition device 10 may obtain an ambient noise signal in a current location space 1202 and perform updating (transfer learning) of the existing noise cancellation model 1241 using the ambient noise signal to obtain the noise cancellation model 141 after updating. Specifically, the voice recognition device 10 may perform updating (transfer learning) of the existing noise cancellation model 1241 using an ambient noise signal obtained in the current location space 1202 and the laboratory sound (predetermined another sound).

Description of FIG. 13

FIG. 13 is a diagram illustrating a process in which a voice recognition device divides a microphone detection signal according to a time segment.

As illustrated in FIG. 13, according to the embodiment of the present disclosure, the voice recognition device 10 may define a time segment in which the user's voice is detected among an entire time segment of the microphone detection signal to a voice segment 1301.

Further, the voice recognition device 10 may define other segments other than the voice segment 1301 in which the user's voice is detected among the entire time segment of the microphone detection signal to a non-voice segment 1302.

For example, the voice recognition device 10 may recognize (obtain) a microphone detection signal detected in the non-voice segment 1302 among the entire time segment of the microphone detection signal as an ambient noise signal.

Description of FIG. 14

FIG. 14 is a diagram illustrating a process in which a voice recognition device updates a noise cancellation model.

As illustrated in FIG. 14, the voice recognition device 10 may update a previously learned noise cancellation model 1400.

For example, the previously learned noise cancellation model 1400 may be defined to an input value 1410, an output value 1430, and a plurality of parameters 1421, 1422, 1423, and 1424 included in a plurality of layers for outputting the output value 1430 from the input value 1410.

For example, the processor 170 may set a log power spectrum (LPS) obtained by normalizing a signal obtained by applying an ambient noise signal obtained from a microphone detection signal to a predetermined voice (clean signal without noise) as an input value 1410.

For example, the processor 170 may set an ideal ratio mask (IRM) of a predetermined voice (clean signal without a noise) as the target output value 1430.

For example, the processor 170 may set a log power spectrum (LPS) obtained by normalizing a signal obtained by applying an ambient noise signal obtained from a microphone detection signal to a predetermined voice (clean signal without a noise) as the input value 1410 and set an ideal ratio mask (IRM) of the predetermined voice (clean signal without a noise) to the output value 1430, thereby updating (or learning) a plurality of previously learned (or previously generated) parameters 1421, 1422, 1423, and 1424 included in a plurality of layers.

Here, when a signal is input in which the ambient noise signal and a predetermined voice are mixed, the processor 170 may update the noise cancellation model so as to output only a predetermined sound. That is, the processor 170 may again learn (update) a plurality of existing parameters learned to output only a laboratory voice when a signal obtained by synthesizing the laboratory voice and a laboratory noise is input so as to output only the laboratory voice when a signal obtained by synthesizing the laboratory voice and an ambient noise of a current space is input.

Description of FIG. 15

FIG. 15 is a flowchart illustrating a detailed example of a process in which a voice recognition device obtains an ambient noise signal.

As illustrated in FIG. 15, the voice recognition device 10 may perform step S130 of obtaining an ambient noise signal of FIG. 10 through steps S1501 to S1509 and a detailed description thereof is as follows.

First, after step S120 of FIG. 10, the voice recognition device 10 may extract a characteristic value from a non-voice segment signal of the microphone detection signal (S1501).

For example, the above-described characteristic value may mean a value listed in the form of a vector by extracting a characteristic value from a specific signal. For example, the characteristic value may mean a log power spectrum of a specific signal described with reference to FIG. 14.

Thereafter, the voice recognition device 10 may input the extracted characteristic value to a previously learned voice detection model (S1503).

Here, when the specific signal is input, the voice detection model may be a previously learned AI neural network model (or deep learning model) so as to output a voice detection probability in which a voice is to be detected from the specific signal.

Thereafter, the voice recognition device 10 may obtain a voice detection probability as an output value of the voice detection model.

Thereafter, the voice recognition device 10 may determine whether the voice detection probability is larger than a predetermined threshold value (S1507).

If the voice detection probability is equal to or smaller than a predetermined threshold value, the voice recognition device 10 may perform again from step S1501.

If the voice detection probability is larger than a predetermined threshold value, the voice recognition device 10 may obtain (recognize) a non-voice segment signal of the microphone detection signal as an ambient noise signal (S1509).

Description of FIG. 16

FIG. 16 is a flowchart illustrating a detailed example of a process in which a voice recognition device updates a noise cancellation model.

As illustrated in FIG. 16, the voice recognition device 10 may perform step S140 of updating the noise cancellation model of FIG. 10 through steps S1601 to S1607 and a detailed description thereof is as follows.

First, the voice recognition device 10 may store an ambient noise signal obtained from step S130 of obtaining the ambient noise signal of FIG. 10 in the memory 140 (S1601).

Thereafter, the voice recognition device 10 may obtain a storage capacity of the ambient noise signal stored in the memory 140 (S1603).

Here, the memory 140 may store another ambient noise signal stored in real time before step S140 as well as a currently obtained ambient noise signal. The voice recognition device 10 may obtain a storage capacity of another ambient noise signal stored in real time before step S140 as well as a currently obtained ambient noise signal stored in the memory.

Thereafter, the voice recognition device 10 may set an update period of the noise cancellation model based on the storage capacity of the ambient noise signal stored in memory 140 (S1605).

Here, the voice recognition device 10 may initially set an update period to a specific value. After the update period is previously set, the voice recognition device 10 may change the predetermined update period according to a storage capacity of the ambient noise signal stored in the memory 140.

For example, when the storage capacity of the ambient noise signal is less than a threshold value, the voice recognition device 10 may maintain the update period of the noise cancellation model to an existing value. For example, when the storage capacity of the ambient noise signal exceeds a threshold value, the voice recognition device 10 may reduce an update period of the noise cancellation model further than a predetermined period.

For another example, whenever the storage capacity of the ambient noise signal exceeds a predetermined capacity, the voice recognition device 10 may set an update period to update the noise cancellation model.

Finally, the voice recognition device 10 may update the noise cancellation model in every predetermined update period (S1607).

Description of FIG. 17

FIG. 17 illustrates an example of a process in which a voice recognition device updates a noise cancellation model in cooperation with a server (5G network).

Unlike a case in which the voice recognition device 10 updates the noise cancellation model without cooperation of an external device, the voice recognition device 10 and a server (5G network), which is one example of the external device may update the noise cancellation model through step S1700 (S1710 to S1760) and a detailed description thereof is as follows.

First, as illustrated in FIG. 17, the processor 170 of the voice recognition device 10 may control the communication unit 110 of the voice recognition device 10 to perform an initial access procedure to the server based on the SSB (S1710).

When the initial access procedure is completed, the server may transmit Downlink Control Information (DCI), which is information to be a basis when the voice recognition device 10 transmits a microphone detection signal to the server to the voice recognition device 10 (S1720). That is, the processor 170 of the voice recognition device 10 may control the communication unit 110 to receive DCI for transmission of a microphone detection signal from the server.

When DCI is received, the processor 170 of the voice recognition device 10 may control the communication unit 110 to transmit a characteristic value of the microphone detection signal based on conditions set to the DCI to the server (S1730).

Thereafter, the server may perform AI processing (S1740) and a detailed description thereof is as follows.

First, the server may input a characteristic value of the microphone detection signal transmitted from the voice recognition device 10 to the voice detection model (S1741).

Thereafter, the server may obtain a voice detection probability, which is a probability that the user's voice is to be detected from the microphone detection signal as an output value of the voice detection model (S1742).

Thereafter, the server may determine whether the voice detection probability is larger than a threshold value based on the output value (S1743).

If the voice detection probability is equal to or smaller than a threshold value, the server may notify the voice recognition device 10 that no voice was detected from the microphone detection signal (S1751).

If the voice detection probability is larger than a threshold value, the server may obtain an ambient noise signal of a non-voice segment from the microphone detection signal (S1744).

Thereafter, the server may update the previously learned noise cancellation model using an ambient noise signal (S1745).

The previously learned noise cancellation model may be a previously learned noise cancellation model by an AI processor (not illustrated) included in the server, and a function thereof is the same as that described above. Further, the previously learned noise cancellation model may be a noise cancellation model configured with the same parameters as those of the previously learned noise cancellation model stored in the voice recognition device 10.

Thereafter, the server may transmit the updated noise cancellation model to the voice recognition device 10 using an ambient noise signal (S1752). That is, the processor 170 of the voice recognition device 10 may control the communication unit 110 to receive the noise cancellation model updated by the server using the ambient noise signal.

The processor 170 of the voice recognition device 10 may remove the ambient noise signal from the microphone detection signal using the updated noise cancellation model (S1760).

Unlike a case in which the voice recognition device 10 updates the noise cancellation model without cooperation of the external device, when the noise cancellation model is updated as illustrated in FIG. 17, the voice recognition device 10 may significantly reduce a data computation amount consumed for an update process of the noise cancellation model.

Description of FIG. 18

FIG. 18 illustrates another example of a process in which a voice recognition device updates a noise cancellation model in cooperation with a server (5G network).

As illustrated in FIG. 18, the voice recognition device 10 and a server (5G network), which is one example of the external device may update the noise cancellation model through step S1800 (S1810 to S1860) and a detailed description thereof is as follows.

Here, steps S1810, S1820, S1830, S1840, and S1851 of FIG. 18 are the same as steps S1710, S1720, S1730, S1740, and S1751 of FIG. 17 and therefore a detailed description thereof will be omitted.

Unlike step S1752 of FIG. 17, the server according to another example of FIG. 18 may transmit parameters of the updated noise cancellation model instead of the noise cancellation model updated using an ambient noise signal to the voice recognition device 10 (S1852). That is, the processor 170 of the voice recognition device 10 may control the communication unit 110 to receive only parameters of the noise cancellation model updated by the server.

The processor 170 of the voice recognition device 10 may update the noise cancellation model using the updated parameters (S1860).

Thereby, compared with a case of FIG. 17, the voice recognition device 10 of FIG. 18 may significantly reduce resources for data exchange with the server.

Description of FIG. 19

FIG. 19 illustrates another example of a process in which a voice recognition device updates a noise cancellation model in cooperation with a server (5G network).

As illustrated in FIG. 19, the voice recognition device 10 and a server (5G network), which is one example of the external device may update the noise cancellation model through step S1900 (S1910 to S1952) and a detailed description thereof is as follows.

Here, steps S1910, S1920, S1930, S1941, S1942, S1943, S1944, S1945, and S1951 of FIG. 19 are the same as steps S1710, S1720, S1730, S1741, S1742, S1743, S1744, S1745, and S1751 of FIG. 17 and therefore a detailed description thereof will be omitted.

Unlike FIG. 17, a server according to another example of FIG. 18 may update the noise cancellation model using the an ambient noise signal and remove the ambient noise signal from the microphone detection signal using the updated noise cancellation model (S1946).

Thereafter, the server may transmit a microphone detection signal in which an ambient noise signal is removed to the voice recognition device 10 using the updated noise cancellation model (S1952). That is, the processor 170 of the voice recognition device 10 may control the communication unit 110 to receive the microphone detection signal in which an ambient noise signal is removed by the server using the updated noise cancellation model.

Therefore, by obtaining a microphone detection signal in which a noise is removed by the server without necessity to directly perform a noise cancellation process, the voice recognition device 10 may minimize a data calculation process consumed for noise cancellation.

Description of FIG. 20

FIG. 20 illustrates another example of a process in which a voice recognition device updates a noise cancellation model in cooperation with a server (5G network).

As illustrated in FIG. 20, the voice recognition device 10 and a server (5G network), which is one example of the external device may update the noise cancellation model through step S2000 (S2010 to S2060) and a detailed description thereof is as follows.

Here, steps S2010, S2020, S2030, S2041, S2042, S2043, S2044, and S2051 of FIG. 20 are the same as steps S1710, S1720, S1730, S1741, S1742, S1743, S1744, and S1751 of FIG. 17 and therefore a detailed description thereof will be omitted.

Unlike FIG. 17, a server according to another example of FIG. 20 may obtain an ambient noise signal from the microphone detection signal and transmit the ambient noise signal to the voice recognition device 10 (S2052). That is, the processor 170 of the voice recognition device 10 may control the communication unit 110 to receive an ambient noise signal obtained by the server.

Thereafter, the processor 170 may update the noise cancellation model using the ambient noise signal (S2060).

As illustrated in an example of FIG. 20, by receiving an ambient noise signal from a server without necessity to directly obtain an ambient noise signal, the voice recognition device 10 may minimize a data computation amount consumed in a process of detecting a sound from the microphone detection signal and obtaining an ambient noise signal.

Description of FIG. 21

FIG. 21 illustrates another example of a process in which a voice recognition device updates a noise cancellation model in cooperation with a server (5G network).

As illustrated in FIG. 21, the voice recognition device 10 and a server (5G network), which is one example of the external device may update the noise cancellation model through step S2100 (S2110 to S2150) and a detailed description thereof is as follows.

Here, step S2110 of FIG. 21 is the same as step S1710 of FIG. 17 and therefore a detailed description thereof will be omitted.

The server according to the example of FIG. 21 may transmit DCI for transmission of an ambient noise signal to the voice recognition device 10 (S2120). That is, the processor 170 of the voice recognition device 10 may control the communication unit 110 to receive DCI for transmitting an ambient noise signal to the server from the server.

Thereafter, the processor 170 of the voice recognition device 10 may control the communication unit 110 to transmit a characteristic value of the ambient noise signal to the server based on conditions set to DCI (S2130).

Thereafter, the server may perform AI processing of the ambient noise signal (S2140) and an example of AI processing is as follows.

The server may update the previously learned noise cancellation model using a characteristic value of the ambient noise signal transmitted from the voice recognition device 10 (S2141).

Thereafter, the server may transmit the updated noise cancellation model to the voice recognition device 10 (S2150). That is, after the voice recognition device 10 transmits the characteristic value of the ambient noise signal to the server, the processor 170 of the voice recognition device 10 controls the communication unit 110 to receive the noise cancellation model updated by the server from the server.

Thereafter, the processor 170 of the voice recognition device 10 may remove an ambient noise signal from the microphone detection signal using the updated noise cancellation model (S2160).

Therefore, the voice recognition device 10 may minimize a data computation amount required for updating the noise cancellation model.

Description of FIG. 22

FIG. 22 illustrates a server for recognizing a voice according to another embodiment of the present disclosure.

According to another embodiment of the present disclosure, a server 30 of FIG. 22 may perform an operation of the server of FIGS. 17 to 21.

As illustrated in FIG. 22, the server 30 for recognizing a voice according to another embodiment of the present disclosure may include a communication unit 310, a power supply unit 390, a memory 340, and a processor 370.

The communication unit 310 may perform wired/wireless communication with an AI device 20 and a voice recognition device 10. That is, the communication unit 310 may transmit and receive signals in which the server of FIGS. 17 to 21 transmits and receives to and from the voice recognition device 10 of FIGS. 17 to 21.

The power supply unit 390 may supply power to each component (the processor 370, the communication unit 310, and the memory 340) of the server 30.

The memory 340 may store data (e.g., the previously learned noise cancellation model) for performing an operation of the server of FIGS. 17 to 21.

The processor 370 may include an AI processor 361 for performing AI processing of the server of FIGS. 17 to 21. The AI processor 361 may access the AI neural network stored in a deep learning model 26 of the AI device 20 or the memory 340 to update the AI neural network and to obtain specific data using the AI neural network.

J. Summary of an Embodiment of the Present Disclosure Embodiment 1

A method of intelligently recognizing a voice of a voice recognition device includes obtaining a microphone detection signal detected using at least one microphone of the voice recognition device; obtaining an ambient noise signal from the microphone detection signal when a voice signal is detected from the microphone detection signal; updating the previously learned noise cancellation model based on the ambient noise; and removing the ambient noise signal from the microphone detection signal using the updated noise cancellation model.

Embodiment 2

In Embodiment 1, the obtaining of an ambient noise signal includes obtaining a signal of a non-voice segment other than a voice segment in which the voice is detected among an entire segment of the microphone detection signal as the ambient noise signal.

Embodiment 3

In Embodiment 2, the updating of the previously learned noise cancellation model includes updating the noise cancellation model in every predetermined period.

Embodiment 4

In Embodiment 3, the period is changed based on a capacity in which the ambient noise signal is stored in a memory of the voice recognition device.

Embodiment 5

In Embodiment 2, the obtaining of an ambient noise signal includes: inputting the microphone detection signal to the previously learned voice detection model; determining whether the voice is detected from the microphone detection signal based on an output value of the voice detection model; and obtaining the ambient noise signal from the microphone detection signal when the voice is detected from the microphone detection signal.

Embodiment 6

In Embodiment 2, the noise cancellation model is previously learned based on another noise signal of a predetermined space and another voice signal of predetermined another user, wherein the updating of the previously learned noise cancellation model includes updating the previously learned noise cancellation model using the another voice signal and the ambient noise signal.

Embodiment 7

In Embodiment 2, the method further includes receiving Downlink Control Information (DCI) used for scheduling transmission of the microphone detection signal from a network; and transmitting the microphone detection signal to the network based on the DCI.

Embodiment 8

In Embodiment 7, the method further includes performing an initial access procedure to the network based on a synchronization signal block (SSB); and transmitting the microphone detection signal to the network through a PUSCH, wherein a DM-RS of the PUSCH and the SSB is QCL for QCL type D.

Embodiment 9

In Embodiment 7, the method further includes controlling a communication unit to transmit the microphone detection signal to an AI processor included in the network; and controlling the communication unit to receive AI processed information from the AI processor, wherein the AI processed information includes data related to updating of the noise cancellation model performed based on the microphone detection signal.

Embodiment 10

In Embodiment 9, the data related to updating of the noise cancellation model include the updated noise cancellation model.

Embodiment 11

In Embodiment 9, the data related to updating of the noise cancellation model include the updated model parameters of the noise cancellation model.

Embodiment 12

In Embodiment 9, the data related to updating of the noise cancellation model include the ambient noise signal.

Embodiment 13

In Embodiment 9, the data related to updating of the noise cancellation model include a microphone detection signal in which the ambient noise signal is removed.

Embodiment 14

In Embodiment 2, the method further includes controlling a communication unit to transmit the ambient noise signal to an AI processor included in the network; and controlling the communication unit to receive AI processed information from the AI processor, wherein the AI processed information includes data related to updating of the noise cancellation model performed based on the ambient noise signal.

Embodiment 15

A voice recognition device for intelligently recognizing a voice according to another embodiment of the present disclosure includes a communication unit for transmitting and receiving data to and from a network; at least one microphone; and a processor for obtaining a detection signal of the microphone, obtaining an ambient noise signal from the microphone detection signal when a voice signal is detected from the microphone detection signal, updating the previously learned noise cancellation model based on the ambient noise signal, and removing the ambient noise signal from the microphone detection signal using the updated noise cancellation model.

Embodiment 16

In Embodiment 15, the processor obtains a signal of a non-voice segment other than a voice segment in which the voice is detected among an entire segment of the microphone detection signal as the ambient noise signal.

Embodiment 17

In Embodiment 16, the processor updates the noise cancellation model in every predetermined period.

Embodiment 18

In Embodiment 17, the period is changed based on a capacity in which the ambient noise signal is stored in a memory of the voice recognition device.

Embodiment 19

In Embodiment 16, the processor inputs the microphone detection signal to the previously learned voice detection model, determines whether the voice is detected from the microphone detection signal based on an output value of the voice detection model, and obtains the ambient noise signal from the microphone detection signal when the voice is detected from the microphone detection signal.

Embodiment 20

In Embodiment 16, the noise cancellation model is previously learned based on another noise signal of a predetermined space and another voice signal of a predetermined another user, wherein the processor updates the previously learned noise cancellation model using the another voice signal and the ambient noise signal.

Embodiment 21

In Embodiment 16, the processor controls the communication unit to receive Downlink Control Information (DCI) used for scheduling transmission of the microphone detection signal from a network and to transmit the microphone detection signal to the network based on the DCI.

Embodiment 22

In Embodiment 21, the processor controls the communication unit to perform an initial access procedure to the network based on a synchronization signal block (SSB) and to transmit the microphone detection signal to the network through a PUSCH, and a DM-RS of the PUSCH and the SSB is QCL for QCL type D.

Embodiment 23

In Embodiment 21, the processor controls the communication unit to transmit the microphone detection signal to an AI processor included in the network, and to receive AI processed information from the AI processor, and the AI processed information includes data related to updating of the noise cancellation model performed based on the microphone detection signal.

Embodiment 24

In Embodiment 23, data related to updating of the noise cancellation model include the updated noise cancellation model.

Embodiment 25

In Embodiment 23, data related to updating of the noise cancellation model include updated model parameters of the noise cancellation model.

Embodiment 26

In Embodiment 23, data related to updating of the noise cancellation model include the ambient noise signal.

Embodiment 27

In Embodiment 23, data related to updating of the noise cancellation model include a microphone detection signal in which the ambient noise signal is removed.

Embodiment 28

In Embodiment 16, the processor controls the communication unit to transmit the ambient noise signal to an AI processor included in a network and to receive the AI processed information from the AI processor, and the AI processed information includes data related to updating of the noise cancellation model performed based on the ambient noise signal.

Embodiment 29

A non-transitory computer readable recording medium is a non-transitory computer-executable component that stores a computer-executable component configured to execute in at least one processor of a computing device and obtains a microphone detection signal, obtains an ambient noise signal from the microphone detection signal when a user's voice is detected from the microphone detection signal, obtains an ambient noise signal from the microphone detection signal when the voice signal is detected from the microphone detection signal, updates a previously learned noise cancellation model based on the ambient noise, and removes the ambient noise signal from the microphone detection signal using the updated noise cancellation model.

Embodiment 30

A server for intelligently recognizing a voice includes a communication unit for transmitting and receiving data to and from a voice recognition device; and a processor for controlling the communication unit to obtain a microphone detection signal detected by at least one microphone of the voice recognition device and to obtain an ambient noise signal from the microphone detection signal when a voice signal is detected from the microphone detection signal, to update the previously learned noise cancellation model based on the ambient noise signal, to obtain the voice in which the ambient noise signal is removed using the updated noise cancellation model and for controlling the communication unit to transmit data related to the updated noise cancellation model to the voice recognition device.

The present disclosure may be implemented as a computer-readable code in a medium in which a program is written. The computer-readable medium includes all types of recording devices in which data readable by a computer system is stored. Examples of the computer-readable medium include a hard disk drive (HDD), a solid state disk (SSD), a silicon disk drive (SDD), ROM, RAM, CD-ROM, magnetic tapes, floppy disks, and optical data storages, and also include that the computer-readable medium is implemented in the form of carrier waves (e.g., transmission through the Internet). Accordingly, the detailed description should not be construed as being limitative from all aspects, but should be construed as being illustrative. The scope of the present disclosure should be determined by reasonable analysis of the attached claims, and all changes within the equivalent range of the present disclosure are included in the scope of the present disclosure.

The effects of the intelligent voice recognition method, voice recognition apparatus and intelligent computing device according to an embodiment of the present disclosure are as follows.

According to the present disclosure, by reflecting ambient noise and reverberation data detected in an environment in which a voice recognition device is located, a noise cancellation model can be updated.

Further, according to the present disclosure, a user's voice can be recognized more clearly than a conventional case using a noise cancellation model updated by reflecting ambient noise/reverberation data.

Further, according to the present disclosure, by performing evolutionary learning by reflecting a previously learned noise cancellation model to data related to a peripheral environment, a voice recognition performance of a voice recognition device can be maximized.

Further, according to the present disclosure, an existing generated noise cancellation model is updated through transfer learning instead of newly generating a noise cancellation model, by using the updated noise cancellation model for noise cancellation, a data calculation amount and a speed required for noise cancellation can be dramatically reduced.

Effects which can be achieved by the present disclosure are not limited to the above-mentioned effects. That is, other objects that are not mentioned may be obviously understood by those skilled in the art to which the present disclosure pertains from the following description. 

What is claimed is:
 1. A method of intelligently recognizing a voice in a voice recognition device, the method comprising: obtaining a microphone detection signal detected using at least one microphone of the voice recognition device; obtaining an ambient noise signal from the microphone detection signal when a voice signal is detected from the microphone detection signal; updating a previously learned noise cancellation model based on the ambient noise; and removing the ambient noise signal from the microphone detection signal using the updated noise cancellation model.
 2. The method of claim 1, wherein the obtaining of an ambient noise signal comprises obtaining a signal of a non-voice segment other than a voice segment in which the voice is detected among an entire segment of the microphone detection signal as the ambient noise signal.
 3. The method of claim 2, wherein the updating of a previously learned noise cancellation model comprises updating the noise cancellation model in every predetermined period.
 4. The method of claim 3, wherein the period is changed based on a capacity in which the ambient noise signal is stored in a memory of the voice recognition device.
 5. The method of claim 2, wherein the obtaining of an ambient noise signal comprises: inputting the microphone detection signal to a previously learned voice detection model; determining whether the voice is detected from the microphone detection signal based on an output value of the voice detection model; and obtaining the ambient noise signal from the microphone detection signal when the voice is detected from the microphone detection signal.
 6. The method of claim 2, wherein the noise cancellation model is previously learned based on another noise signal of a predetermined space and another voice signal of a predetermined another user, wherein the updating of a previously learned noise cancellation model comprises updating the previously learned noise cancellation model using the another voice signal and the ambient noise signal.
 7. The method of claim 2, further comprising: receiving Downlink Control Information (DCI) used for scheduling transmission of the microphone detection signal from a network; and transmitting the microphone detection signal to the network based on the DCI.
 8. The method of claim 7, further comprising: performing an initial access procedure to the network based on a synchronization signal block (SSB); and transmitting the microphone detection signal to the network through a Physical Uplink Shared Channel (PUSCH), wherein a DM-RS of the PUSCH and the SSB is quasi co-located (QCL) for QCL type D.
 9. The method of claim 7, further comprising: controlling a transceiver to transmit the microphone detection signal to an AI processor included in the network; and controlling the transceiver to receive AI processed information from the AI processor, wherein the AI processed information comprises data related to updating of the noise cancellation model performed based on the microphone detection signal.
 10. The method of claim 9, wherein the data related to updating of the noise cancellation model comprise the updated noise cancellation model.
 11. The method of claim 9, wherein the data related to updating of the noise cancellation model comprise updated model parameters of the noise cancellation model.
 12. The method of claim 9, wherein the data related to updating of the noise cancellation model comprise the ambient noise signal.
 13. The method of claim 9, wherein the data related to updating of the noise cancellation model comprise a microphone detection signal in which the ambient noise signal is removed.
 14. The method of claim 2, further comprising: controlling a transceiver to transmit the ambient noise signal to an AI processor included in a network; and controlling the transceiver to receive AI processed information from the AI processor, wherein the AI processed information comprises data related to updating of the noise cancellation model performed based on the ambient noise signal.
 15. A voice recognition device for intelligently recognizing a voice, the voice recognition device comprising: a transceiver for transmitting and receiving data to and from a network; at least one microphone; and a processor for obtaining a detection signal of the microphone, obtaining an ambient noise signal from the microphone detection signal when a voice signal is detected from the microphone detection signal, updating a previously learned noise cancellation model based on the ambient noise signal, and removing the ambient noise signal from the microphone detection signal using the updated noise cancellation model.
 16. The voice recognition device of claim 15, wherein the processor obtains a signal of a non-voice segment other than a voice segment in which the voice is detected among an entire segment of the microphone detection signal as the ambient noise signal.
 17. The voice recognition device of claim 16, wherein the processor updates the noise cancellation model in every predetermined period.
 18. The voice recognition device of claim 17, wherein the period is changed based on a capacity in which the ambient noise signal is stored in a memory of the voice recognition device.
 19. The voice recognition device of claim 16, wherein the processor is configured to: input the microphone detection signal to a previously learned voice detection model, determine whether the voice is detected from the microphone detection signal based on an output value of the voice detection model, and obtain the ambient noise signal from the microphone detection signal when the voice is detected from the microphone detection signal.
 20. The voice recognition device of claim 16, wherein the noise cancellation model is previously learned based on another noise signal in a predetermined space and another voice signal of a predetermined another user, wherein the processor updates the previously learned noise cancellation model using the another voice signal and the ambient noise signal. 